Every day we ask AI to draft, summarize, and explain. But when does a chatbot’s answer become a document you can trust? Drawing on a conversation with ChatGPT, Sascha Donner shows how humans confer the trust, force, and answerability that turn an AI answer into an accountable document.
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Based on a conversation in the InfoFire series between computer scientist Ricardo Baeza-Yates and information scientist Shalini R. Urs, this article explores the philosophical, cognitive, and societal shifts that have accompanied generative AI’s evolution from an external tool to an internal “cognitive companion.”
It essentially outlines five core arguments regarding how AI is reshaping human thought:
• The Great Illusion (Imitation vs. Comprehension): This section highlights the “ELIZA Effect,” in which people mistake generative AI’s fluent, polished language for genuine understanding. Baeza-Yates prefers the term “Computational Intelligence” to “Artificial Intelligence,” arguing that large language models generate language statistically. Seduced by linguistic fluency, we increasingly confuse prediction with cognition.
• The Delegation of Mind (Cognitive Offloading): Unlike older technologies that automated physical tasks or basic mathematics, generative AI risks “cognitive substitution” or “cognitive surrender.” By outsourcing intellectual exercises such as writing, coding, and reasoning to AI, humans may lose the productive cognitive struggle required to form original thoughts and develop critical judgment.
• Truth in the Age of Prediction: Information retrieval, such as through traditional search engines, preserves human agency and verification, whereas AI generation encourages immediate acceptance. LLMs do not “hallucinate” out of malfunction; they “confabulate” because their objective is statistical probability rather than factual truth. Detaching credibility from reality creates an “era of algorithmic plausibility.”
• Intelligence without Experience: AI systems process language but lack physical embodiment, causal reasoning, common sense, and “tacit knowledge” gained from living in the physical world. Furthermore, because training data relies heavily on dominant digital structures, such as English, it creates “AI colonialism”—a quiet standardization that compresses global cultural and intellectual diversity into a single dominant narrative.
• Education in the AI Era: Because AI can immediately perform lower-order cognitive tasks, such as summarizing and basic programming, education must shift from testing memory to cultivating human judgment and metacognition. Baeza-Yates suggests pedagogical changes such as requiring students to actively critique and debug AI-generated answers or bringing back oral examinations.
Ultimately, the article serves as a warning that AI should be used to amplify human intellect, not atrophy it; the greatest danger is that humanity might gradually surrender the very cognitive capacities that define its existence.
For most of human history, the hard part was making enough food. Then we got very good at it, and spent the next fifty years discovering that abundance has its own diseases. We have just done the same thing to text: generative AI made producing it close to free, while our capacity to read it has not moved an inch. I don’t think the answer is to write less out of guilt, but somebody has to be on the reading side of this, and right now the incentives are paying all of us to stand on the other one.
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What happens when students can write a literature review, analyze information, and receive instant feedback with a few prompts? What happens when they make a summary of literatures and answers questions based on the documents they upload? What happens when they find relevant questions and answers for discussion in their thesis work using Grok? Yes, these are possible and researchers are using these tools in effective and productive ways.
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The people cheering the dancing robot and the people warning about the killer robot think they are on opposite sides. They are running the same script, watching a human-shaped body instead of the system. Real AI harm has been arriving without a face, without an announcement, and without anyone identifiable to blame.
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R. David Lankes has written here and posted a preprint, submitted to the Journal of Documentation, arguing that generative AI is causing an “Information Shock”: a disruption so deep that it dissolves the document — the field’s organizing unit — and demands a new information science. It is the best account we have of what generative AI does to the documentary foundations of information science, and I agree with nearly all of it. The distinction between AI that produces document-like objects and AI that co-authors exchanges is the sharpest framing of the problem in our literature. The argument that AI literacy carries document-evaluation over intact and aims it at something that is not a document is correct and overdue. What follows is not a rebuttal but an extension — because the most important sentence in his argument is nearly its last, and it stops one step short of a tradition built for exactly this moment.
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Have you ever asked ChatGPT a question just to get a quick answer? You’re not alone. Millions of people now turn to AI assistants before opening Google, visiting a website, or asking a librarian for help. College students are no different. Instead of beginning a research assignment by searching for books, articles, or websites, many now begin by asking a generative AI assistant a question and receiving a polished response within seconds. It feels much faster and easier. Yet this seemingly simple shift raises an important question: if research increasingly starts with an answer instead of a search, how do learners move from information to understanding?
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We are living in the early stages of an Information Shock caused by generative AI. Information Shocks are rare, but historically transformative. This one will require a reinvention of information science itself.
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Have you ever searched online for a health symptom late at night and ended up reading long discussion threads from strangers on Reddit or Quora? Maybe you were looking for reassurance, wondering whether someone else had experienced the same thing, or simply trying to avoid another stressful trip to the doctor. For millions of people, online Q&A platforms have quietly become part of everyday health information seeking.
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This InfoFire conversation is with Ravi Venkatesan—business leader, social entrepreneur, author, and one of India’s most influential voices on leadership and inclusive development. Having led organizations as diverse as Cummins India, Microsoft India, Bank of Baroda, and the Global Alliance for Mass Entrepreneurship (GAME), while also serving on the boards of institutions such as Rockefeller Foundation, Hitachi, and Harvard Business School, Venkatesan brings a rare perspective that spans business, public policy, education, and social innovation. What makes his perspective particularly compelling is his conviction that AI is not merely a technological phenomenon but a societal one, requiring us to rethink markets, institutions, education, and entrepreneurship together.
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