In the race toward AGI, many entrepreneurs are wondering where they can still build lasting value when frontier models are advancing at breakneck speed.
I’ve been exploring this question through a simple framework:
The key is finding domains where you can build systems that transform data into verifiable outcomes.
Telecom RAN (Radio Access Network) optimization is a perfect example. Here’s why:
Why RAN Optimization Presents a Unique AI Opportunity:
Real-world complexity that can’t be simulated: Network performance depends on physical environments, user behavior, and hardware interactions that frontier models can’t access.
Closed-loop validation: You can measure the real impact of optimizations in live networks, creating proprietary feedback loops.
Clear ROI: Small improvements in energy efficiency, spectrum utilization, or coverage translate directly to operator cost savings.
Eight Specific AI Agent Opportunities:
Energy Optimization: Reducing power consumption while maintaining QoS
Interference Management: Maximizing SINR through intelligent coordination
Load Balancing: Distributing users optimally across network resources
Predictive Maintenance: Forecasting equipment failures before they happen
QoS Optimization: Tailoring network parameters to specific application needs
Coverage Optimization: Eliminating dead zones through intelligent configuration
Spectrum Efficiency: Maximizing bits/Hz/second through dynamic allocation
Self-Healing Networks: Automating recovery from service disruptions
The Strategic Advantage:
By building these systems, you create:
Proprietary datasets from actual network operations
Domain-specific knowledge that compounds over time
Solutions that solve real business problems today
You can use Pydantic AI Agent framework and build a verifiable reward based closed looped reinforcenent learning system.
While the giants focus on general intelligence, the opportunity for entrepreneurs lies in building intelligent systems in domains with physical constraints, specialized knowledge, and clear success metrics.
The future belongs to those who can bridge the gap between AI capabilities and the messy, complex realities of critical infrastructure.
What domain-specific AI Agent opportunities are you seeing in your industry?
Most network issues could be resolved if an AI-based agent could eliminate discrepancies in site configurations caused by incorrect parameter values.
Traditional methods such as dump parsing, parameter audits with golden parameters, and similar processes are still in use.
Another key area is traffic balancing, but for AI to be effective, it must understand configuration thresholds per cell and determine when a user should transition to another cell within the same RAT or a different RAT.
Trying to work heavily in this area on live network.
Instead of focussing the AI to understand thresholds per KPI, I recommend to use Domain Expertise to define acceptable ranges of thresholds and everything from the observable KPIs and metrics as Network intent. (Like bler on pusch becomes uplink capacity. Same uplink capacity intent is mapped to uplink PRB usage too).
This way, you need to define the intent only. (I want to improve downlink capacity but can only compreomise on coverage, uplink capacity by upti XX thresholds). This becomes the definition of your reward function.
I did this when 5g was new and even vendors did not know which parameter baseline may be the best. I used it to discover parameter values which were better than overall vendor suggestions.
In addition to this, two more cases for me are reporting (write a detailed event report for Eid in area Xxx). And integration of knowledge base articles as vector database to improve search of related terms and ideas. (Eventually for a LLM with large context window to always have such information present when trying to determine veat thresholds based on documentation).
You can leverage Pydantic AI Agent frameworks to build a verifiable reward-based closed-loop reinforcement learning system.
While tech giants focus on general intelligence, the best opportunities for entrepreneurs lie in specialized domains with physical constraints, clear success metrics, and real-world complexity.
The same logic applies to game AI—think about games environments where reinforcement learning models adapt to player behavior dynamically. The intersection of AI and real-world complexity is where true innovation happens.
