Telecom's Silver Bullet for 5G Monetization

Is a Connected Universe the next quantum leap in AI, and will it spark 5G transformation?

I have previously explored how a true AI doesn't just follow a pre-programmed set of instructions; rather, it learns, adapts, and evolves based on data, patterns, and experiences, very much like an infant.

A newborn doesnt arrive in the world with a full understanding of language, emotions, or societal norms. Instead, they observe, experience, and are taught by their parents and environment. They see interactions, imitate behaviors, and learn through reinforcement - understanding what works and what doesnt based on responses from those around them. Similarly, an AI learns from what it sees (initial set of data), its experiences (algorithms), and what it is taught (human collaboration & input).

Just like an infant, AI's understanding deepens with exposure and feedback. Where an infant learns from multiple sources - watching their parents, interacting with their surroundings, and receiving direct instruction - AI gathers information in a variety of ways. It observes patterns in data, receives guidance from human feedback, and learns from interactions with users and other systems. Some AI models are trained by analyzing vast amounts of text, images, and videos. Others improve through trial and error, testing different responses and refining their understanding based on success and failure. However, the primary source of data is the most critical aspect of an AI's learning journey. The more diverse and current a data source is, the more intelligent, competent, and responsive an AI can be.

Before the internet, human knowledge was limited to books, libraries, and direct conversation - information that can quickly become outdated. The World Wide Web changed this by making vast amounts of current, diverse knowledge accessible, accelerating learning, and improving decision-making. AI is going through a similar transformation. It must move beyond static data and tap into dynamic, real-time information to become significantly smarter.

Traditional AI models trained in isolation could only learn from pre-existing, fixed datasets. While effective in recognizing patterns, these models struggled to adapt to new trends, unexpected scenarios, or evolving insights. Today's more advanced AI systems (Perplexity, Google Gemini, ChatGPT with Web Browsing, BloombergGPT, CrowdStrike Falcon, etc.) don't just learn once and stop - they are designed to update their knowledge, refine their predictions, and improve based on the latest available data.

However, the real breakthrough will come only when an AI is connected not just to live data but also to other AI systems, forming an intelligent self-improving network. AI can reach new levels of intelligence when it can freely exchange information with other AI systems across industries and domains.

Imagine two cars approaching an intersection - one driven by a human and the other controlled by an AI-powered self-driving system. The human driver, momentarily distracted by a phone notification, fails to notice that the light has turned red and drives into the oncoming traffic. The self-driving car, relying on its sensors and pre-programmed algorithm, can anticipate traffic behavior based on historical data and make smart decisions but cannot fully predict erratic human choices.

Since the human driver's reaction is unpredictable, the AI car is forced to assume multiple possibilities that the human will stop abruptly, swerve, or accelerate through the red light. This uncertainty creates a dangerous situation, increasing the risk of a collision simply because the self-driving car lacks real-time insight into the human driver's immediate intent.

Now, imagine a different scenario where both vehicles are connected and communicating in real time. The self-driving car instantly receives a signal, indicating that the human driver-driven vehicle is not slowing down as expected. Instead of relying solely on its own senses and pre-learned behaviors, the AI car adjusts proactively, either slowing down or rerouting to avoid a potential collision.

If all vehicles were AI-powered and connected, they could coordinate with each other dynamically, adjusting speed and lane positioning to reduce congestion, eliminate last-minute breaking, and prevent accidents caused by human unpredictability, leading to safer, more efficient transportation systems.

Some readers may argue that vehicle-to-everything (V2X) is an alternative to a fully connected AI ecosystem, yet even V2X heavily relies on data from other connected systems to function effectively. While V2X attempts to predict the behavior of non-connected road users through census and AI-driven modeling. It still depends on real-time data shared by other connected vehicles, infrastructure, and networks to build a comprehensive situation awareness. Without a broader connected ecosystem, V2X alone would lack the depth of information needed to make truly intelligent, proactive decisions, reinforcing the necessity of a seamlessly interconnected AI universe where systems continuously exchange and refine data for optimal efficiency and safety.

Hence, the next big leap in AI isn't just about smarter models - it's about seamlessly interconnected intelligence that can learn, adapt, and collaborate across systems, industries, and domains. The creation of an interconnected AI universe - a world where AI models don't just process live data but also communicate and share insights with each other will be essential in transitioning AI from being a passive tool to an autonomous decision-maker, reshaping everything from business strategy to government policies. Of course, it will also raise many eyebrows and frown lines as we must consider several aspects, such as the energy requirement for this kind of endeavor, ethical considerations, necessary guardrails versus bureaucratic roadblocks, and privacy and security risks. Many of which I will be covering in my future posts.

However, the one thing I would like to touch on briefly in conclusion - and elaborate further in my next article is the role that emerging network standards like 5G and 6G will play in the quantum growth of AI. It's not just about how these advanced networks will supercharge AI by providing ultra-fast, low latency, and highly connected infrastructure. It's also about how a connected AI landscape is a long-awaited silver bullet for CSP to monetize their 5G investments. The telecom industry has spent years searching for viable options to monetize their 5G networks, and a connected AI ecosystem presents the much-desired game-changing opportunity with real-time intelligence, hyper-personalized offerings, and predictive automation.

For a connected AI universe to function efficiently, enabling edge computing is no longer optional - it's essential. AI systems require real-time processing to respond instantly to dynamic environments, whether it is autonomous driving industrial automation or smart city infrastructure. However, this is a two-way dependency just as telecoms need AI-powered solutions to deliver enhanced services and drive new revenue streams that justify the 5G pricing model; AI needs fast, ubiquitous networks with low latency and high efficiency to create and exist in a connected world.

In my next article, I will dive deeper into how CSPs can prepare for this connected AI-driven opportunity - the business model that will unlock 5G and 6G monetization and the strategic steps telecom operators must make to stay ahead in the future powered by the connected AI ecosystem.