You Don’t Command AI. You Communicate With It.

Why Human–AI–Machine Interdependency Changes Everything

One of the key reasons I deliberately moved away from the idea, and ultimately the reality, of human–machine codependency is that my research consistently failed to support it as a viable long-term interaction model. Instead, the conclusions validated a different structure altogether: a human–AI–machine interdependent triangular relationship. This shift was not semantic, nor philosophical for its own sake. It emerged from observing how AI systems actually function when they are used effectively, and how they fail when they are treated as conventional machines.

At the centre of this transition is a simple but widely misunderstood principle: you do not use AI by commanding it.

Traditional machines respond to instructions. You tell them what to do, they execute, and the interaction terminates. There is no interpretation and no ambiguity. Humans do not communicate with machines in this model; they issue orders. This paradigm has shaped human–machine interaction for decades, and it has been remarkably successful within its constraints. However, that same paradigm breaks down the moment interpretation becomes part of the system.

AI changes this completely.

Whether we are discussing a natural language model or a computer vision system processing environmental and sensor data, the underlying mechanism is interpretation. Inputs are received, contextualised, and transformed. Meaning is inferred rather than dictated. In this sense, AI does not behave like a machine in the classical sense at all. It behaves like an interpretive system.

This distinction becomes clearer when we consider non-linguistic AI systems. A conventional neural network embedded in a physical environment does not receive typed commands. The environment does not issue instructions. A camera captures light. A sensor detects motion, pressure, temperature, or sound. That raw environmental data is interpreted within context and transformed into something actionable. The system functions because it is designed to make sense of signals, not to follow orders.

Language functions in exactly the same way.

When a human interacts with an AI system through language, the words themselves are environmental data. Tone, framing, intent, and context are part of the signal. This leads to what I often describe as my first golden rule when working with LLMs: always communicate with AI verbally. Aloud, and linguistically. Always. Treat language as data, not as syntax.

The failure to grasp this is why so much discussion around prompt engineering remains superficial. Prompt engineering is often framed as a technical skill, when in reality it is a communication skill. The quality of the output is constrained by the quality of the input, not in terms of length, but in terms of meaning.

A simple human example illustrates this more clearly than any technical explanation. If I tell my partner that I am going out, I have technically communicated something. However, the information content is minimal. All she knows is that I will not be present.

There is no context, no intent, and no opportunity for interpretation or response.

If instead I explain that I am going to the supermarket to buy bread and milk, and then ask whether we need anything else, the interaction changes completely. She may tell me that we do not need milk. She may ask for something different. The final outcome is richer, more accurate, and better aligned with reality. This is not because I spoke more, but because I communicated properly.

AI systems respond in exactly the same way. When a user provides vague, decontextualised input, the system is forced to infer too much. The result is generic output. When a user provides intent, constraints, and perspective, the system has something meaningful to interpret. The output improves accordingly.

This dynamic becomes especially visible in multimodal systems such as image generation. A prompt that simply describes an object or action leaves most decisions to the model and produces unremarkable results. 

"Draw me an image of a woman walking in a street"

However, a prompt that communicates atmosphere, narrative, aesthetic intent, and framing allows the system to interpret rather than guess. The difference is not technical sophistication on the part of the model. It is communicative sophistication on the part of the human.

"A cinematic night scene set on a Parisian street in France during a dark, stormy evening just after heavy rain. The pavement is wet and reflective, with vivid neon lights from nearby signs and streetlamps shimmering across the water on the ground. A single woman walks confidently down the street, centred in frame, her silhouette elegant and deliberate."

This also explains why some individuals adapt to AI systems almost immediately while others struggle. In practice, good managers tend to use AI well. People who are accustomed to articulating goals, providing context, and thinking in terms of outcomes rather than instructions transition naturally. People who struggle to communicate clearly with other humans often encounter the same difficulties with AI. This is uncomfortable, but it is consistent.

AI does not respond to authority. It responds to articulation.

This is why the concept of human–AI–machine interdependency matters. Humans contribute judgement, intent, and contextual understanding. AI contributes interpretation and reasoning. Machines contribute execution and scale. None of these components functions optimally in isolation, and none can be reduced to a command hierarchy.

What is perhaps most striking is that none of this represents a radical new requirement. The importance of clear communication has always been understood. AI simply removes the buffer that previously absorbed poor articulation. It reflects back exactly what it is given, without assumption.

If AI systems appear disappointing, vague, or inconsistent, the issue is rarely the technology itself. More often, it is a failure to communicate meaningfully with an interpretive system.

AI does not reward commands. It rewards clarity, context, and intent. And in doing so, it exposes something that was already there: the quality of our communication.

Ultimately, what AI is doing is collapsing the distance between intent and execution. For decades, that gap was bridged by programming languages, formal syntax, and rigid abstraction layers. Learning to “speak” Python, C++, or any other language was less about expressing intent and more about accommodating machine constraints. AI reverses that dynamic. It allows intent to be communicated directly, in human language, and interpreted rather than compiled. This does not make technical knowledge irrelevant, but it fundamentally changes where value lies. The skill that now matters most is not fluency in syntax, but fluency in communication.

As this transition accelerates, it becomes increasingly clear that the people who will extract the most value from AI are not necessarily the best coders, but the best managers, thinkers, and communicators. Those who can articulate goals, frame problems, and provide meaningful context will optimise AI systems far more effectively than those who rely on instruction alone. In that sense, AI is not replacing human capability; it is amplifying it, and in doing so, it is elevating communication from a soft skill to a central technical competence.