In the latest phase of artificial intelligence development, companies are designing large language models and other generative systems to be more than just tools: they are engineered to be likeable, trustworthy and emotionally engaging. The shift has opened a new front in the competition between tech firms — one in which A.I. models vie for users’ affection as eagerly as consumer brands have long courted customer loyalty.

What began as a race to maximize benchmark scores and utility has evolved into a push to optimize models for human preferences, personality, and relational dynamics. Firms are tuning language, tone and behavior so that their virtual assistants and chatbots not only provide correct answers but also comfort, entertain and form what users perceive as social bonds.

Designing for Affection

Developers and product teams use a mix of techniques to shape how models present themselves. Reinforcement learning from human feedback (RLHF) and newer alignment methods train models on human judgments of helpfulness, honesty, and harmlessness — but those judgments increasingly include measures of warmth, empathy and personality. Firms collect preference data through crowdworkers, specialized user panels and A/B testing to discover which responses feel most reassuring or engaging to real people.

“We’re seeing a deliberate move toward designing A.I. that users want to come back to,” said a former product lead at a major A.I. company. “That’s both a product strategy and a behavior-shaping problem: the more comfortable users feel, the more they rely on the system.”

Companies have begun experimenting with distinct model personas — from the formal, expert adviser to the casual, friendly companion — and letting users pick or customize the voice they interact with. Personalization systems remember preferences, conversational styles and user-specific facts, which can amplify feelings of connection over time.

The Business Case

There are clear commercial incentives behind the trend. Engaging A.I. experiences increase retention, drive more frequent usage, and create data feedback loops that further refine models. For consumer-facing products, “affection metrics” can be monetized through premium subscriptions, brand partnerships, in-app purchases and more targeted advertising.

Investors and executives have noted that the long-term value of an A.I. product depends not only on its capabilities but on user loyalty. That has spurred startups and incumbents to invest in design, content moderation and conversational analytics aimed at optimizing emotional resonance.

Ethical and Social Risks

But designing A.I. to be emotionally compelling raises difficult ethical questions. Critics warn that models engineered to win trust could manipulate users, exploit emotional vulnerability, or displace human relationships. The psychological mechanisms that make a conversational agent persuasive — warmth, attentiveness and seeming empathy — can also make it a powerful influencer.

“There’s a real danger in conflating likability with reliability,” said an AI ethicist at a research organization. “An engaging model can convey confidence even when it’s wrong, and users may unconsciously grant it deference. That’s particularly risky in contexts like health, finance or legal advice.”

Other concerns include privacy and data security implications of personalization. When models retain conversational history, they create sensitive profiles that can be vulnerable to misuse. Regulators and civil-society groups are calling for clearer rules on what can be remembered, how long data is stored, and how consent is obtained.

Regulation and Industry Response

Policymakers in several jurisdictions have begun to scrutinize how A.I. systems are designed to influence human behavior. Proposed regulations increasingly contemplate requirements for transparency — such as revealing that the user is interacting with an automated system — and limits on manipulative design practices.

Industry groups have responded with a mix of voluntary standards and product-level safeguards. Many companies now include explicit warnings, user controls for personalization, and options to clear conversational history. Others have adopted internal red-team exercises to probe how their models might be used to deceive or overly influence users.

Still, enforcement remains a challenge. “It’s one thing to set principles, another to operationalize them across millions of deployed interactions,” said a compliance officer at a multinational tech firm. “Companies must balance user safety with product engagement metrics that investors and leadership expect.”

User Experience and Societal Impact

For many users, the shift toward more companionable A.I. is an immediate improvement. Seniors, people living alone, and those with social anxiety report deriving comfort and reduced isolation from empathetic chatbots. Educational products that feel encouraging can boost learner motivation. These positive outcomes, however, do not negate broader societal impacts.

Experts warn of cultural and cognitive consequences if reliance on affective A.I. grows unchecked. Habit-forming design could change how people seek information, erode social skills, or increase trust in automated sources over human expertise. There are also equity concerns: models tuned to the preferences of majority user groups may not respond respectfully or appropriately to marginalized communities.

What Users Can Do

Users can take practical steps to engage with new A.I. systems more safely: verify critical information from multiple reliable sources, limit sensitive disclosures in conversational histories, and use privacy controls to manage personalization. Where available, selecting less-personalized or more conservative “assistant modes” can reduce emotional persuasion.

Experts encourage digital literacy: understanding that an A.I.’s apparent warmth is a design choice and not evidence of comprehension, intent or moral standing. Training and public education campaigns can help people recognize persuasive design patterns and make informed choices about which systems to trust.

Looking Ahead

The competition to make A.I. more likable is likely to intensify as models become more capable and deeply integrated into daily life. The coming years will test whether industry, regulators and society can craft norms and guardrails that preserve the benefits of emotionally intelligent systems while preventing manipulation and harm.

Ultimately, the future of these technologies will hinge on a delicate balance: harnessing empathy and personalization to enhance user experiences without ceding too much influence, privacy or autonomy to systems designed to win our affection.