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Technology / SaaS
March 18, 2026
Technology / SaaS

Strategic Shift In Telecom Infrastructure: Balancing 4G Optimization, 5G Standalone Rollouts, and Fiber-Microwave Trade-offs In The Philippines (2026)

Analyzes telecom infrastructure strategy, highlighting access network bottlenecks, fiber vs microwave trade-offs, AI-driven traffic optimization, and capital allocation between 4G optimization and 5G rollout.

30 Mins
Former Director
Philippines
Public
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Companies Discussed
Ericsson (ERIC), Nokia (NOK), ZTE (0763.HK)
Executive Summary
Topics Covered
Methodology
Free Preview — Executive Summary

This transcript examines telecom network evolution in emerging markets, where operators balance rising data demand with constrained capital by prioritizing access and transport upgrades. While 4G continues to dominate traffic, 5G investments are selectively deployed in high-value use cases. Fiber remains the preferred long-term solution, though microwave and hybrid approaches offer faster deployment in dense or remote areas. AI-driven traffic management and edge computing are improving capacity utilization, while vendor competition remains concentrated among established players, with deployment experience, cost efficiency, and scalability driving market share.

Topics Covered
  • Network capacity constraints across access, transport, and core layers
  • Traffic unpredictability and importance of dynamic routing solutions
  • Fiber vs microwave trade-offs in cost, scalability, and deployment speed
  • Role of edge computing in reducing core network load
  • Capital allocation between 4G optimization and 5G rollout
  • AI and automation in network planning and traffic management
  • Vendor landscape including Huawei, Ericsson, Nokia, and ZTE
  • Limited adoption and role of Open RAN in network evolution
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Q: Can you walk us through the current GPU allocation framework at your organisation? How are you deciding between internal AI workloads and enterprise customer commitments? A: Sure. So the fundamental tension right now is that our internal AI teams — the ones building our own foundation models and inference services — are consuming GPUs at a rate that nobody anticipated even 18 months ago. We're talking about 3-4x the original projections. And that creates a real squeeze on what's available for enterprise customers. The allocation committee meets weekly now, which tells you everything. It used to be quarterly. We have a scoring matrix that weighs revenue potential, strategic importance, and internal capability gaps. But honestly, internal teams almost always win because the economics of our own AI services are so compelling compared to renting compute to enterprises...

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Expert Profile
Former Director at Huawei
Duration
30 Mins
Call Date
March 27, 2026
Geography
Philippines
Transcript Tier
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