Tech, first person you talk to

On most evenings, Ananya23 opens ChatGPT for reasons that have nothing to do with work. Sometimes, she asks for the remedy for a mild headache that would not go away. On other days, it is about a hangover cure that does not involve “drink more water.” On tougher nights, it is advice on how to phrase an uncomfortable message to her boss, or whether it is normal to feel anxious before a Monday morning meeting. Initially, she got templated, and quick answers. Over the months, ChatGPT became the first stop for emotional reflection, venting about work, rehearsing difficult conversations with friends, and even exploring career decisions.

What started as a productivity tool has become something more consistent, and reliable. It is now a companion for conversations. ChatGPT has quietly become the first stop for daily questions, emotional reassurances, and low-stakes medical advice. Not that people, including Ananya23, trust it blindly. The fact is that the AI tool responds instantly, without judgement, and in a calm and familiar tone. When Ananya23 hit the usage limit on the free version, she upgraded because “it feels like a conversation that remembers me.” This shift from utility to relationship is exactly where the purpose and business of AI tools seem to head towards.

In addition, this transition reflects a broader behavioural shift in how millions, especially youngsters, use generative AI. ChatGPT’s early adoption story was simple. It was faster than search, better at drafting emails, and surprisingly good at summarising documents. Over time, the usage drifted from productivity to personal. ChatGPT serves 800 million active users globally, making it one of the fastest-adopted consumer technologies in history. Of these, 35 million are subscribers who pay. What is more striking is not the scale but that most of payment-linked upgrades are driven by continuity.

A recent reel posted by influencer, Apoorva (better known online as The Rebel Kid), went viral when she joked about hitting ChatGPT’s free usage limit during a late-night conversation, and promptly upgraded to a paid plan. The reason for the virality was not the content or drama but the relatability. Many upgrade free subscriptions for similar uses. Now that the ChatGPT Go plan is free the trend is more prevalent. This highlights something that the platforms understand well. Once a digital product becomes embedded emotionally, the users tend to pay to keep it uninterrupted.

This playbook is not new. Spotify resorted to a similar strategy. The music streaming platform did not get subscriptions because it had more songs. It expanded and spread because it learned how the users felt, say, on a Tuesday evening, and served the right playlist. Instagram did not grow because of filters but because it became a place where identity, validation, and belonging played out daily. ChatGPT’s value lies in emotional reliability, the sense that it understands context, remembers preferences, and responds in a steady, and essentially non-judgement way.

From a business standpoint, this engagement is more powerful than feature upgrades. The former inculcates habit, stickiness, and willingness to keep paying or upgrading. But conversational intimacy brings risks, and this is where the industry’s learning curve is visible. As ChatGPT becomes a space for emotional and mental health conversations, concerns about over-reliance have moved from the abstract to reality. OpenAI acknowledges that more than a million users engage in suicide-related conversations every week. In 2024, a case in the UK involved a teenager, and triggered debates around how AI responds to users in emotional distress.

While details were handled cautiously by the authorities and media, the episode became a reference point for regulators and platform designers. In the US, a lawsuit by a family alleged harmful reinforcement through AI conversations. Such cases highlight how conversational AI, when treated like a confidant rather than a tool, operates in a different risk category. Unlike search engines, it does not simply surface links or neutral information. It responds in sentences, mirrors emotional tones, and offers reassurances. This quality makes it useful, and raises the stakes.

For AI firms, these episodes underscore a difficult reality. Once users talk to machines the way they talk to people, platforms inherit a level of responsibility that traditional software did not consider. Hence, platforms like OpenAI have begun to strengthen safety mechanisms built directly into conversation flows. These include flagging distress signals, redirecting users to professional resources, and limiting certain types of responses. From a business standpoint, this is not about ethics. It is about sustainability and momentum. Guardrails require investments. They slow model deployment, increase complexity, and demand constant fine-tuning.

Yet they are unavoidable. Trust, regulatory compliance, and brand reputation depend on them. Much like the social media platforms had to invest heavily in content moderation, conversational AI firms are discovering that responsibility by design is not optional, but part of the cost of doing business at scale. Emotional connection is a double-edged sword. The opportunity is clear but complex. Emotional engagement drives growth and earnings, but demands restraint and accountability. The next phase of competition may not be about who has the smartest model, but builds a trustworthy experience.

For regulators, this shift raises questions. How can AI handle medical or mental health conversations? Where does assistance end and intervention begin? These debates have started. For users, the convenience is undeniable. Talking to AI is easier than calling a friend, faster than booking an appointment, and often less intimidating than asking questions publicly. Whether it is advice for a hangover, lingering stomach issue, or thinking out loud at the end of a long day, AI fits neatly into everyday lives.

But as interactions deepen, they invite reflection on where comfort turns into dependence. More than a decade ago, Black Mirror explored a similar idea in the episode, ‘Be Right Back,’ where a woman used AI trained on her late partner’s digital footprint to recreate conversations with him. The technology worked. The responses were right. The comfort was real. Yet the episode’s quiet discomfort comes from the realisation that familiarity is not the same as understanding, and responsiveness is not the same as presence.

Conversational AI is nowhere near that form of fictional extreme. But the parallel is instructive. When people begin to rely on machines for personal and intimate reasons, the question is not about capability. It is about boundaries. This balance may ultimately define the future scope of conversational AI.