Generative AI is becoming a layer between people and the information they seek. Tools such as ChatGPT can gather material, summarize it and deliver a tailored response, saving effort while influencing what users notice, remember and trust.
From finding information to interpreting it
Earlier internet tools made vast amounts of information available, but people still had to search through results and decide what mattered. Generative AI goes further: it can locate information, evaluate it, combine it and present a response that feels ready to use.
Personalization adds another dimension. ChatGPT’s custom responses let users save instructions about their purpose and preferred style. Meta AI, meanwhile, can converse, generate images and perform tasks across WhatsApp, Messenger and Instagram. In each case, the tool can be shaped around a person’s needs.
That convenience may make AI feel less like a reference tool and more like a regular companion. Mustafa Suleyman, an artificial intelligence researcher and co-founder of DeepMind, has described personalized AI as a relationship, comparing it to a friend or coach. A familiar, always available assistant could become part of how people work through questions and choices.
The internet already changed how we remember
The effects of technology on thinking are not entirely new. Since the internet entered everyday life, people have gained immediate access to information on subjects ranging from banking to travel. Research has linked this connection to changes in cognition, memory and creativity.
One example is the “Google effect.” Online search can improve our ability to find information while reducing how much of that information we remember. Knowing where to retrieve an answer can become more useful than storing the answer itself.
That shift has potential benefits. When people do not need to remember every detail, they may have more mental capacity for problem solving and creative thinking. But online searching has also been associated with distractibility and dependency. It can raise people’s confidence in their knowledge even when they have not deeply engaged with the information.
Generative AI may intensify this tradeoff because it supplies a synthesized answer, rather than a list of results to inspect. Users can spend less time collecting information, but they may also have fewer natural prompts to check how an answer was assembled or what perspectives it leaves out.
Familiar answers can earn too much trust
AI responses can sound familiar, objective and engaging. Those qualities may make them easier to accept, even when a response deserves closer scrutiny. Automation bias describes the tendency to overestimate the reliability of machine-produced information. The mere exposure effect also helps explain why information can seem more trustworthy when it feels familiar or personal.
Social media offers a cautionary example. In a 2016 study, Facebook users said they felt more “in the know” based on how much news content appeared online, rather than how much they had actually read. Seeing more information can create a sense of awareness without ensuring understanding.
Personalized systems can also narrow what people encounter. Social media feeds filtered around users’ interests can limit exposure to diverse content. That narrowing has been linked to greater ideological polarization, less consideration of alternative perspectives and a higher likelihood of encountering fake news.
AI tools raise related concerns about data ownership, bias and misinformation. Google added source links to AI-generated search summaries from its Search Generative Experience after the tool drew criticism for inaccurate and problematic responses. Links can help people inspect sources, but users still have to decide whether the answer and its evidence hold up.
Use AI while keeping judgment in the loop
Generative AI could support personalized learning, speed up writing and information analysis, and contribute to scientific discovery. It may also help people communicate and connect, and sometimes provide synthetic companionship. These possibilities depend in part on how people and organizations choose to use the technology.
AI literacy is one practical starting point. Understanding both people’s and AI’s strengths and weaknesses can help users treat generated responses as material to assess, rather than conclusions to accept automatically. Human-led quality control matters when a response will shape understanding or decisions.
Design choices matter too. Tools can be built to support human autonomy and critical thinking, including by making it easier to examine the information behind an answer. The broader challenge is to gain the speed and convenience of AI without letting a personalized response stand in for independent thought.