The vision and promise

The telecom sector has been embracing a bold narrative: adopt large-scale AI models and integrate them into the Radio Access Network (RAN), in hopes of unlocking new business models. This includes the idea of a “Network Foundation Model” fed with massive amounts of network data, and an “AI-RAN” that runs advanced AI on the same infrastructure as radio.

The analyst pushback

But according to the recent article in RCR Wireless News, this vision is “a costly engineering and economic trap.”

The article highlights three major flaws:

• Physics vs probability gap: Foundation models rely on statistical patterns, while telecom systems are governed by deterministic physics and strict protocols. A guess that seems plausible is not acceptable in network operations.
  
• Drift tax: Network conditions evolve (new spectra, protocols, hardware), so the model must constantly retrain and adapt. Maintenance costs may overwhelm benefits.
  
  
• Correlation fallacy: The business case for AI-RAN assumes idle infrastructure (GPUs at cell sites) can monetise AI workloads. Reality: network load and AI load often peak together, so empty capacity may not be there.

What this really means

What all this means is that while the idea of AI-powered telecom infrastructure sounds futuristic, practical constraints are being ignored. It is not enough to transplant a large language-model mindset into network infrastructure and expect it to work. The article argues a more realistic view is needed: modular tools tuned for telecom tasks, not one giant general-purpose model.

Implications for industry

Telecom operators and vendors might risk wasted investment if they fall for the hype without rigorous cost-benefit work. They will need to ask: does this solve a real problem in my network? Can I measure ROI? Are the operational risks manageable?

The takeaway

For strategy leads in tech and telecom, the lesson is: don’t chase the idea of “AI everywhere” without aligning to domain-specific realities. The future of network-AI is likely in specialized, pragmatic tools—not in heroic monolithic models.