The proliferation of sensors, especially cameras, will generate massive amounts of video data. This data must be uploaded to cloud AI models for processing, making robust upstream bandwidth—not just downstream—the critical new infrastructure bottleneck and a significant opportunity for telecom companies.

Previous technology shifts like mobile or client-server were often pushed by technologists onto a hesitant market. In contrast, the current AI trend is being pulled by customers who are actively demanding AI features in their products, creating unprecedented pressure on companies to integrate them quickly.

Unlike the speculative "dark fiber" buildout of the dot-com bubble, today's AI infrastructure race is driven by real, immediate, and overwhelming demand. The problem isn't a lack of utilization for built capacity; it's a constant struggle to build supply fast enough to meet customer needs.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

While on-device AI for consumer gadgets is hyped, its most impactful application is in B2B robotics. Deploying AI models on drones for safety, defense, or industrial tasks where network connectivity is unreliable unlocks far more value. The focus should be on robotics and enterprise portability, not just consumer privacy.

The next wave of data growth will be driven by countless sensors (like cameras) sending video upstream for AI processing. This requires a fundamental shift to symmetrical networks, like fiber, that have robust upstream capacity.

The excitement around AI capabilities often masks the real hurdle to enterprise adoption: infrastructure. Success is not determined by the model's sophistication, but by first solving foundational problems of security, cost control, and data integration. This requires a shift from an application-centric to an infrastructure-first mindset.

While spending on AI infrastructure has exceeded expectations, the development and adoption of enterprise-level AI applications have significantly lagged. Progress is visible, but it's far behind where analysts predicted it would be, creating a disconnect between the foundational layer and end-user value.

AT&T's CEO reframes the network debate, stating that fiber is the universal backbone. Technologies like 5G and satellite are simply different methods for connecting end-users to this core fiber infrastructure, not true competitors to it.