AI can predict, respond and persuade.. but does it actually understand? In this blog, we explore the philosophical and technical cost..

Does It Matter If AI Doesn’t Understand Context?

This blog examines a question that has become one of the most pressing questions in technical philosophy since the boom of democratised AI. As someone who studied philosophy at university, before moving onto Data Science, I find myself in a position close to obligation to cover this subject.

The central issue under consideration here is not whether artificial intelligence can generate plausible responses, write coherent prose, or even pass professional exams. These feats, impressive as they are, do not necessarily imply understanding. The deeper question is this:

Does it matter that AI does not genuinely understand the context in which it operates?

Regardless of what answer we assign this question, deeper thought and questions are provoked. The purpose of this blog is not to make you agree with me, it is to bring awareness to the problems of blind reliance on AI.

What AI Seems To Understand

It is important to acknowledge a hard truth: contemporary AI systems, including state-of-the-art language models, do not understand language in any cognitive or semantic sense. These systems operate by identifying statistical patterns across massive datasets. Models such as GPT-4, Claude and Gemini do not contain mental representations or grounded concepts. They do not know what a tree is or what it means to be cold. Instead, they generate likely continuations of text based on distributions learned during training.

Despite this limitation, these systems produce results that appear remarkably competent. They write essays, summarise reports, compose emails and answer questions with fluency and coherence. This performance creates an illusion, a cognitive mirage that tempts us to treat statistical prediction as semantic understanding. However, understanding, as philosophers from Wittgenstein to Searle have argued, is not merely about producing correct outputs. It involves grasping meaning, intention, consequence and often the unsaid. The Chinese Room thought experiment remains relevant here: a system may follow rules to manipulate symbols, but it does not know what those symbols mean. The distinction between syntax and semantics, therefore, remains unresolved in practical AI.

Pattern Recognition Is Not Cognition

A compelling argument sometimes made is that understanding itself is simply a product of complex pattern recognition. In this view, humans are, at their core, biological prediction machines. Therefore, if AI can recognise patterns at sufficient depth and scale, perhaps that is all the understanding we need. But this view glosses over the embodied and socially situated nature of human cognition. We do not merely recognise patterns; we live within them and shape them through lived experience.

Human understanding naturally condenses information. When you hear, "The ball broke the window," you don’t predict the next event mathematically—you just know what it means. You retrieve a structured interpretation grounded in physics, social norms and personal memory. You infer agency, causality and likely outcomes. An AI, in contrast, unfolds a distribution of similar sentence structures. It may choose the correct continuation, but it does not know why one outcome is more plausible than another beyond statistical association.

This is more than an academic concern. In engineering terms, a lack of grounded understanding can lead to brittle systems. An AI may recommend a treatment but fail to recognise contraindications. It may suggest a course of action without acknowledging its ethical or legal implications. These are not merely hypothetical risks, but active failures waiting to occur in domains such as healthcare, law, education and autonomous navigation.

Why Shallow Understanding Isn’t Safe

It is worth reflecting on a broader cultural shift. Much of the modern digital infrastructure is already built upon shallow approximations. Click-through rates are treated as curiosity. Page views are substituted for quality. Engagement becomes a substitute for value. In such a world, it may not be surprising that we are increasingly willing to accept superficial AI understanding as good enough.

If our standards for comprehension erode, so too might our expectations of accountability, agency and truth. When we lower the bar for what qualifies as understanding, we risk embedding that superficiality into the very systems we rely on to make sense of the world. The implications go far beyond technical performance - they touch on how we govern, how we educate, how we diagnose and how we relate to one another. If we are willing to accept systems that merely simulate coherence, then we also risk accepting shallowness in our own discourse and institutions.

The challenge is not merely that AI lacks understanding, but that we might forget to demand it, in ourselves and in our tools. The danger lies in accepting performance as equivalent to meaning and in confusing the appearance of coherence with actual cognition. Once that threshold is crossed, we begin to build a culture where authenticity is secondary to fluency and where decisions are made not on the basis of understanding, but on the appearance of intelligence.

The Risks Of Misunderstanding In Practice

In technical applications, the limits of AI’s contextual awareness become more than a philosophical curiosity. They become operational hazards. Consider AI in a legal setting. A model may generate language that looks persuasive, use plausible information and present it professionally. Yet it lacks an understanding of justice, proportionality, or social consequence. It does not know what fairness is, nor can it assess whether a decision is morally coherent.

Similarly, in education, models may produce detailed answers to exam questions, write essays, and simulate explanations. But they do not know what it means to learn. They cannot diagnose misunderstanding, adjust tone for motivation, or engage in pedagogical reasoning. Their outputs may appear fluent, but their internal processes are fundamentally opaque and devoid of learner empathy.

In creative domains, the cracks are subtler but still significant. AI can compose music, generate art, or write stories. But its creations are not rooted in cultural memory, emotional need, or human aspiration. They are derivations from training data, plausible echoes of works made by people who did understand. The risk is not simply that these outputs will fail technically, but that they may subtly erode our expectations of what it means to create meaningfully and originally.

Why The Illusion Of Understanding Matters

The real concern here is epistemological. If we increasingly accept AI outputs as authoritative, we risk allowing simulation to displace interpretation. A system that appears correct becomes a source of truth, regardless of whether it is grounded. Over time, we may stop asking why something is true and start asking only what the model says.

This is not a critique of the technology itself. Machine learning is a powerful achievement. But it is not sentient. It is not ethical. It does not possess intention or awareness. And the more seamlessly it integrates into human workflows, the more vital it becomes to remember what it lacks.

So, yes, it does matter if AI does not understand context. It matters because we live in a world that desperately needs more comprehension, not less. (see: Rise of disinformation, geopolitical disconnects, divided societies). It matters because judgment cannot be outsourced to systems that do not grasp consequence.

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