The India AI Impact Summit—a major global gathering on artificial intelligence—took place at Bharat Mandapam in New Delhi from February 16 to 20, 2026 (with some extensions to the 21st due to overwhelming response). I won’t delve into the event’s controversies or logistics here. Instead, let’s turn to a more essential question: What exactly is artificial intelligence?
When we want to know something—anything—AI rapidly pulls together relevant information from the vast digital world and delivers it in a clear, organized form. That’s not especially remarkable anymore; AI can scan enormous datasets almost instantly and produce a reasoned answer or conclusion. What’s far more intriguing is how it can create or refine creative content—a letter, essay, poem, or story—in precisely the style, tone, or voice we specify.
So, where do the similarities and differences lie between human thought/consciousness and AI? Which is truly unique—AI or human consciousness? How does AI solve problems? And most importantly: Can AI tell us anything about events that haven’t yet occurred, or about things never recorded digitally? The answer is no.
AI has no insight into the future and no knowledge of undocumented events. It remains entirely ignorant about anything absent from the digital record. It can only speak to what has already happened and been captured, stored, and made accessible online.
When we pose a question to AI, this is what happens behind the curtain: it instantly combs through all relevant available digital information, conducts an extremely detailed analysis of patterns, connections, and context, then selects and presents what it calculates as the most satisfactory or probable response.
The same logic powers its creative output. The foundation remains digital text—millions upon millions of letters, essays, stories, poems, articles, and beyond. To draft a letter, AI analyzes countless archived examples, extracts common structures, tones, phrasing, and conventions, then synthesizes them probabilistically to match your request. The process is identical for stories, poems, or essays: provide a topic, and it composes by drawing on patterns learned from vast corpora of human writing.
This core mechanism is known as Large Language Models (LLMs). At heart, an LLM operates like an ultra-advanced form of grammar. Grammar provides the rules for arranging words into meaningful sentences—a meaningful collection of words following those rules. LLMs scale this enormously: they perform billions of probabilistic operations—additions, subtractions, and recombinations—across sentence structures, word choices, and meanings to produce coherent, contextually appropriate text.
This echoes George Boole’s 1854 book The Laws of Thought, which showed how logical reasoning can be formalized through algebraic operations. Logic and mathematics are, at their root, efforts to model and explain the physical world, moving from the known to the unknown. Even childhood “fill in the blank” exercises share a distant kinship with algebraic problem-solving, where the unknown (“X factor”) emerges from combinations of known elements.
Today’s AI represents the long evolution of that journey. That’s why AI-generated text often feels remarkably smooth and polished: it has internalized and recombined the styles, arguments, insights, and expressions of countless great writers, thinkers, philosophers, and scientists. Whatever we request, AI can usually deliver something strikingly close—almost instantly. But once more: if something was never documented or digitized, AI has no access to it and cannot address it.
How, then, does AI understand our personal tastes and preferences so well? This is where algorithms enter the picture. AI learns our likes and dislikes only through our digital interactions—searches, clicks, likes, views, purchases, dwell time, skips, and scrolls. The process bears a striking resemblance to Freudian psychoanalysis. In psychoanalysis, the therapist uncovers buried subconscious material—repressed thoughts, forgotten memories, hidden desires—through conversation, free association, and attentive listening, entering the patient’s inner world to reveal what was previously inaccessible.
AI performs an analogous operation in the digital domain. Through algorithms, it associates and dissociates behavioral patterns: what we watch or skip, like or dislike, linger on or avoid. From these traces, it constructs a remarkably accurate model of our preferences. That’s why our feeds, recommendations, and ads often feel uncannily personalized.
Yet clear limits remain. Two stand out above all:
Ethics and moral reasoning
AI can outline ethical dilemmas, cite examples, and summarize philosophical viewpoints, but it lacks any personal sense of right and wrong. It possesses no inner moral compass.
Original, creative thought
AI cannot produce truly novel ideas independently. Every advancement in AI relies on humans first introducing new concepts, discoveries, theories, art, or insights. Without ongoing human originality, AI stays static—it can only recombine the existing. While it excels at organizing information, suggesting pathways, or linking distant ideas, the genuine leap of insight, intuition, and imagination belongs to the human mind.
In short: AI is an extraordinarily powerful mirror reflecting humanity’s collective digital output—but it is not, and cannot become, a substitute for human consciousness, moral judgment, truly original thought, or meaningful engagement in jurisprudence and complex legal reasoning.