The TAM Nobody Saw Coming: Is AI Networking Really a $200B Opportunity?
May 7, 2026
ONES & SONiC
TL;DR
AI networking is no longer just a conversation among hyperscalers. According to 650 Group co-founder Alan Weckel, the market is heading toward a $200B opportunity driven by AI infrastructure, Ethernet growth, observability, automation, and open networking.
What makes this shift different is that software is becoming just as important as hardware, especially as enterprises begin building AI-ready networks at scale.
Introduction
In a recent podcast discussion with Ilona Gabinsky from Aviz Networks, the conversation explored how AI is fundamentally changing networking economics, architecture, and operations.
The biggest takeaway wasn’t just faster hardware or larger clusters. It was the realization that networking is becoming the operational backbone of the AI era, connecting GPUs, applications, telemetry systems, automation layers, and future autonomous infrastructure.
And unlike previous cloud waves, this shift is expanding far beyond hyperscalers into enterprise and neo-cloud environments.
Why Is the AI Networking TAM Growing So Fast?
Because AI infrastructure requires entirely new network architectures and far more operational intelligence than traditional data centers.
According to Alan Weckel, AI networking barely existed at the start of this decade. By the end of it, the market could surpass $200 billion.
What’s accelerating growth:
- AI backend networks
- Ethernet-based AI fabrics
- Massive telemetry requirements
- GPU/XPU interoperability
- Automation and orchestration software
- Enterprise AI adoption
One of the most important observations from the discussion was that networking growth is no longer just about “speeds and feeds.”
Instead, organizations now need complete AI networking solutions that combine:
| Infrastructure Layer | Why It Matters |
|---|---|
| High-speed Ethernet fabrics | Connect large-scale AI compute |
| Observability | Detect bottlenecks and failures |
| Automation | Handle operational complexity |
| Open networking | Enable interoperability |
| AI-driven operations | Reduce human scaling limits |
This is why software is becoming a major part of the TAM alongside hardware.
How Big Is the Software Opportunity in AI Networking?
Software could represent nearly half of the total AI networking opportunity.
During the conversation, Alan estimated that within a future $100B+ networking market, software-related value could account for roughly $40–50 billion.
That includes:
- Network observability
- Telemetry platforms
- AI-driven operations
- Automation layers
- Orchestration systems
- Open networking software stacks
The reason is simple: humans cannot manually operate AI-scale infrastructure efficiently anymore.
As AI clusters grow larger and workloads become more dynamic, operators need:
- Real-time telemetry
- Faster fault isolation
- Predictive automation
- Self-healing capabilities
- AI-assisted troubleshooting
This is pushing networking from a hardware-centric market toward a software-defined operational model.
Why Is Open Networking Becoming More Important in the AI Era?
AI environments are too complex to run on isolated, proprietary networking stacks.
One of the strongest themes in the discussion was the rise of open networking.
According to Alan, organizations are adopting open networking faster because:
- The technology has matured
- AI infrastructure requires interoperability
- Operators now manage multiple network layers simultaneously
Modern AI environments may include:
- Backend AI fabrics
- Frontend data center networks
- DCI infrastructure
- GPU clusters
- Different accelerator vendors
Managing all of that with disconnected proprietary systems becomes operationally difficult at scale.
Open networking helps standardize interoperability across infrastructure layers while enabling organizations to integrate different hardware and software ecosystems more efficiently.
And as AI infrastructure expands, that flexibility becomes increasingly valuable.
Are Enterprises Already Adopting AI for Network Operations?
Not fully but the market is clearly moving in that direction.
Alan described the current phase as early adoption:
- Proof of concepts
- Pilot deployments
- Limited production rollouts
Most enterprises are still evaluating AI operational models before broad deployment. But the trajectory is becoming clear.
The long-term vision points toward increasingly autonomous operations, including:
- AI-assisted configuration validation
- Automated troubleshooting
- Intelligent telemetry analysis
- Self-driving networks
Today, many AI networking tools function as operational copilots. Over time, they are expected to evolve into far more autonomous systems.
Why Are Analysts So Bullish on AI Networking?
Because networking is becoming the connective fabric of the entire AI economy.
One of the most interesting points from the podcast was the idea that networking is often underestimated because it has “always existed.”
But AI changes the role of the network completely.
Without scalable networking:
- AI clusters cannot communicate efficiently
- Agentic systems cannot coordinate
- GPU resources become underutilized
- Physical AI systems cannot scale reliably
That’s why networking is increasingly being viewed as the “nervous system” of AI infrastructure rather than simply connectivity hardware.
And with enterprise AI adoption still early, many analysts believe the market expansion has only just begun.
Conclusion
The AI era is not simply about creating larger networks; it is about creating entirely new operational requirements for infrastructure.
The shift toward:
- open networking,
- deep observability,
- automation,
- and AI-driven operations
It is redefining how networking platforms are built and managed.
What once looked like a traditional infrastructure category is now evolving into one of the largest software and operational opportunities in enterprise technology.
And if the current trajectory continues, AI networking may become one of the defining infrastructure markets of the decade.
Frequently Asked Questions
How big is the AI networking market expected to get and what is driving it?
Alan Weckel from 650 Group puts the number at over 200 billion dollars by the end of the decade. AI networking barely existed at the start of it. What is pushing the growth is not just faster hardware. It is backend AI networks, Ethernet-based fabrics, massive telemetry needs, GPU interoperability, automation software, and enterprises starting to build AI-ready infrastructure at scale.
Why is software becoming just as important as hardware in AI networking?
Humans cannot manually run AI-scale infrastructure anymore. Clusters are too large, workloads shift too fast, and the operational complexity is beyond what any team can handle by hand. Software covering observability, telemetry, automation, orchestration, and AI-driven operations is what keeps the whole thing running. Alan estimated software could account for 40 to 50 billion dollars of a future 100 billion dollar plus networking market.
Why is open networking picking up so fast in AI environments?
AI infrastructure is too complex for isolated proprietary stacks. A modern AI environment might include backend AI fabrics, frontend data center networks, DCI infrastructure, GPU clusters, and multiple accelerator vendors all at once. Managing all of that with disconnected proprietary systems at scale becomes a nightmare. Open networking standardises how those layers talk to each other without forcing teams into one vendor's ecosystem.
Where are enterprises right now in adopting AI for network operations?
Early days. Most are still running proof of concepts, pilot deployments, or limited production rollouts. Broad deployment has not happened yet but the direction is clear. The long-term vision points toward autonomous operations including automated troubleshooting, intelligent telemetry analysis, AI-assisted configuration validation, and eventually self-driving networks. Right now most tools work as operational copilots. That will change.
Why do analysts say networking is now the nervous system of AI infrastructure?
Without scalable networking, AI clusters cannot communicate properly, GPU resources sit underutilised, and agentic systems cannot coordinate at all. Physical AI systems cannot scale reliably without it. Networking used to be background infrastructure. In the AI era it is the connective fabric the whole economy runs on. That is a completely different role and a completely different level of importance.
Is AI networking only a hyperscaler conversation or does it apply to regular enterprises too?
The podcast makes this point clearly. Previous cloud waves were dominated by hyperscalers. AI networking is expanding well beyond that into enterprise and neo-cloud environments. Enterprises are already starting to build AI-ready networks at scale and that adoption is still early, which is exactly why analysts think the biggest part of the market growth has not happened yet.
What does a complete AI networking solution actually need to include?
Five things come up: high-speed Ethernet fabrics to connect large-scale compute, observability to catch bottlenecks and failures fast, automation to handle operational complexity, open networking for interoperability across vendors, and AI-driven operations to reduce the limits of human scaling. Hardware alone is not enough. All five layers working together is what makes an AI network actually function at scale.
Why has networking been underestimated as an AI infrastructure category until now?
The podcast puts it well. Networking has always existed so people assume they already know what it is. AI changes the role completely. It is no longer just connectivity hardware sitting in the background. Every GPU job, every agentic workflow, every telemetry stream runs through it. Once that clicks, the market opportunity becomes obvious and the previous underestimation looks like a missed signal in plain sight.
Learn more about how Aviz Networks is approaching AI-era infrastructure with open networking, observability, and AI-powered network operations.
