Does Scale Equal Understanding?

Before, it was hard to talk to machines. We had to learn commands, software, interfaces, programming languages to make them do what we wanted. AI removes that obstacle by offering something uniquely human: Understanding.

In plain, simple human language.

Large language models have enabled AI’s capability of understanding. As AI system scales, it is being forced, by the sheer diversity and contradiction of human text, to build internal representations that reconcile conflicting evidence. It begins to recognize patterns across domains, connect concepts, transfer knowledge between contexts, infer intent and generate solutions to problems it has never explicitly encountered before. What once required discussion with a specialist to gain an understanding of the problem now begins with a conversation with AI. 

This provocative possibility stares at us: Understanding may not be separate from scale, understanding may emerge because of scale.

But what does it even mean to understand something at all? This argument is not really about AI. It's about what understanding actually is and whether humans have it in any purer sense than machines do. 

Understanding, even in humans, is pattern recognition, just instantiated in our biological hardware and nurtured in the richness of data since the day we were born. When you understand the concept of "loss," you are drawing on a vast network of associated experiences, emotions , narratives and contexts. You are pattern-matching against everything you have ever felt, read, lived and witnessed. The question then becomes whether the system of patterns used by AI is rich enough, specific enough and generalized enough to constitute something functionally equivalent to comprehension. And that's why, scale matters. 

However, no matter how large the model or how rich the corpus, this can only be sophisticated pattern matching. Calling it "understanding" is a category error: like saying a thermostat understands temperature because it responds to it reliably. There is one thing AI cannot obtain: Experience.

I once had this debate with a friend: can learning be achieved without experience? 

At first, the answer seemed obvious. How can anyone start learning without experiencing the world? Since we were babies, humans learn by touching, failing, succeeding, observing, feeling, and interacting. We learn what "hot" means partly because we have been burned. We learn what cuts because we have touched sharp things. We learn falling is hurt because of our scars. AI Language models have none of this. Their patterns are patterns of patterns, derived entirely from the linguistic residue of human experience, not from experience itself. Experience fosters knowledge.

But the more we debated through it, the more I realized that much of what we know does not come directly from our own experiences. We learn history without witnessing it. We learn about distant countries without visiting them. We understand scientific concepts that exist far beyond our senses. A large portion of human knowledge is acquired through language, stories, books, and the accumulated experiences of others. In a sense, the large part of learning has always been a process of absorbing experiences that are not our own. 

But Learning and Understanding are two entirely different things.

Learning is the acquisition of knowledge. Understanding is the ability to make sense of that knowledge. Someone can learn the rules of a language and yet fail to understand culture, the irony in a joke, or the silence between two people who once loved each other. The gap is not fueled by obtaining more information but by obtaining lived experiences. 

This is where scale runs into a wall. A model trained on a million examples has processed more grief than any single human ever could. It has read every metaphor for loss, every cultural ritual, every stage of mourning across every tradition. And yet it has never waited by a hospital bed. Never held the hand of someone leaving. Never felt the specific, irreplaceable weight of a particular absence. 

It can't be denied that large language models have demonstrated an extraordinary capacity for learning. They absorb vast amounts of information, recognize patterns, and generate responses that often appear insightful. But whether this large scale of learning amounts to human understanding remains an open question. 

Humans do not simply learn from data. We learn through living. Our understanding is shaped by experiences, emotions, relationships, failures, and consequences. Learning becomes understanding when it is grounded in reality, when you have the exposures. And it is hard for AI to replicate this process through the scale of information alone.

AI scale does not guarantee understanding. But it may be the very mechanism by which understanding becomes possible. It is genuinely up to you whether the difference matters and whether we understand ourselves enough to know the difference. 

#AI

© 2020-2026 Narrativ.Design. All rights reserved. The author is the Co-founder and Head of Strategy at Narrativ.Design. She is relentlessly driven by the passion to explore human insights and foster people’s behavioral changes. Her approach aims to find the sweet-spot between business performance and positive cultural impact. Connect with her at connect@narrativ.design.