When AI fails to deliver what we expect, we often blame it for being "buggy" or "unreliable." But perhaps the problem isn't with AI - it's with our mental model. We keep trying to use AI like software, when it behaves much more like a human colleague.

The "AI is not software" argument correctly identifies what AI isn't - but stops short of recognizing what it is. AI doesn't just lack the properties of software; it actively exhibits human-like behaviors. This isn't anthropomorphism. It's a practical observation with immediate implications for how we work with these systems.

Non-Determinism as Feature

The same prompt produces different outputs. This variability isn't a failure of engineering - it's what enables creativity. Temperature settings function like energy levels. Context windows create attention limits. Previous exchanges influence future responses. These aren't metaphors; they're functional parallels to human cognition.

Software bugs are deterministic - reproduce the conditions, reproduce the bug. AI "errors" are stochastic - the same prompt that produced brilliance might next produce nonsense. This isn't broken software; it's tired human. The fix isn't debugging but understanding conditions for optimal performance.

The Instruction Paradox

Over-constrained prompts degrade output quality. Add twenty requirements to a prompt and watch coherence collapse - precisely how humans respond to micromanagement. The optimal instruction level follows human management principles: clear intent with execution freedom.

Consider this progression:

• "Write about dogs" - too vague
• "Write 500 words about golden retrievers including history, temperament, care requirements, health issues, training tips, and famous examples, using formal tone but accessible language, with three citations" - over-constrained
• "Write a comprehensive guide about golden retrievers for potential owners" - optimal

Software improves with more specifications; AI, like humans, improves with better context and clearer goals.

Personality Without Source Code

Claude differs from GPT differs from Gemini in consistent, learnable ways. These aren't different programs but different personalities. We develop working relationships with specific models just as we adapt to colleagues' work styles.

Claude might refuse tasks with ethical concerns while GPT eagerly attempts them. Gemini might excel at technical explanations while Claude prefers conceptual frameworks. These patterns persist across users and sessions - personality traits, not random variations.

The black box isn't a bug - we navigate human black boxes daily through pattern recognition and relationship building. We don't need to understand neuron weights any more than we need to understand synaptic connections.

Authorship Void

OpenAI can't patch GPT's behaviors any more than parents can patch their children. Training creates tendencies, not deterministic outputs. Fine-tuning shifts probabilities, not certainties. No one "owns" the output in the software sense - not the training company, not the fine-tuner, not the prompter.

When GPT produces problematic output, OpenAI can't push a hotfix. They can only retrain - a months-long process that might fix one issue while creating others. RLHF isn't debugging; it's parenting. System prompts aren't configuration files; they're organizational culture.

This distributed non-ownership mirrors human responsibility. When an employee makes an error, blame doesn't trace cleanly to parents, schools, or employers. Accountability becomes negotiated and contextual.

The Management Shift

The image stitching example crystallizes this: when AI fails, our instinct is to grab the keyboard. But this is a manager taking over a subordinate's task. The effective response was providing visualization tools for self-diagnosis - classic management through capability building.

Three principles emerge from this shift:

Don't grab the keyboard. When AI struggles, resist the urge to do it yourself. This scales poorly and prevents learning. Instead, guide discovery.

Provide context, not commands. "The image has stitching artifacts" is unhelpful. "Generate a visualization showing how you're positioning each tile" enables self-diagnosis.

Build capabilities, not patches. Teaching AI to visualize its own work for debugging is reusable meta-learning. Fixing one specific coordinate calculation is a patch that doesn't transfer.

This isn't about being nice to AI. It's about recognizing that management techniques work because AI exhibits the same patterns that make management necessary for humans: variable performance, context-dependence, learning through guidance rather than programming.

We Need AI Management Alongside Human Resource Management

The software paradigm gave us version control, debugging, and deterministic outputs - none of which apply to AI. The human paradigm gives us management, coaching, and relationship building - all of which do.

Just as Human Resource Management emerged as a discipline when organizations recognized that people weren't interchangeable parts in a machine, we now need AI Management as a formal field of study. This isn't about anthropomorphizing technology - it's about recognizing which frameworks actually work.

AI Management would study optimal context provision, capability building through iterative interaction, performance variance patterns, and the dynamics of human-AI collaboration. 

The question isn't whether this anthropomorphism goes too far, but whether our software-centric mental models have held us back. Frustrations with AI "hallucination" or "inconsistency" might simply be category errors - expecting software behavior from something fundamentally more human-like in its operations.

We're not programming machines anymore. We're managing artificial minds. The sooner we formalize this discipline, the better we'll be at extracting value from these remarkable, frustrating, human-like systems.