Most discussions about AI focus on the technology’s potential, but the actual impact depends entirely on which company is holding the steering wheel. We are currently seeing two extremes: a global conglomerate betting on massive scale and a solo founder attempting to navigate the early stages of a brand-new business.
Reliance is currently embedding AI directly into its telecom infrastructure, seeking to reach its network of over 500 million subscribers. Instead of creating a new app, they are weaving AI into the backend of the services people already use, aiming to automate tasks like customer support and data analysis at the network level.
The mechanics of backend integration
To integrate AI at scale, companies like Reliance are focusing on deploying large language models (LLMs) closer to the network edge, reducing latency so that voice-based AI responses feel instantaneous during a call. Rather than relying on simple text-based chatbots, they are utilizing deep integration into their API architecture to allow AI to query real-time user data—like location, account status, or usage history—when providing an answer. This requires a robust pipeline that sanitizes user data into a format the model can process, while keeping compute costs low enough to remain sustainable for 500 million users.
In contrast, the new startup venture led by the former Allbirds CEO highlights the difficulty of building from zero. Even with significant seed capital, there is a fundamental difference between having the budget to hire a team and having a product that provides utility. Money funds the experiment, but it does not solve the engineering challenge of building a proprietary data set that makes an AI model distinct from existing commercial alternatives.
For a regular user, the takeaway is clear: the most useful AI tools are often those that require no separate installation. If a service becomes smarter while you are simply making a phone call or browsing an app, that is a genuine integration of utility. Everything else is just a software project still searching for a problem to solve.
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