Who Owns AI's Ideas?

Here's a question that's about to matter a lot: when an AI helps you discover something, who gets the credit?

This isn't hypothetical anymore. AI systems are writing code, finding patterns in data, even proposing new scientific hypotheses. The old frameworks for attribution—built when tools were dumb—are breaking down.

The reflexive answer is to treat AI like we treat other tools. When you write with Microsoft Word, Microsoft doesn't co-author your novel. But this analogy is getting stretched thin. Modern AI doesn't just record your thoughts—it generates new ones.

Some want to give AI co-authorship. This is both too much and too little. Too much because it treats AI as an independent agent with its own goals. Too little because it misses what's actually happening.

The real insight is that attribution has always been about architecture, not execution.

Think about Newton. We don't credit him with inventing gravity. We credit him with building the conceptual framework that let us understand it. He designed the architecture; nature filled in the details.

This pattern repeats throughout science. Gödel didn't create logical limitations—he built the framework that revealed them. Shannon didn't invent information—he designed the system for measuring it.

The same principle applies to AI collaboration. What matters isn't who generates the final output, but who designs the conditions that make discovery possible.

When a researcher uses AI to explore solution spaces or recognize patterns, they're not delegating authorship. They're extending their cognitive architecture—like a mathematician extends their thinking through notation or a scientist through instruments.

The key is architectural intentionality. The human who crafts the investigation, sets the parameters, and recognizes which outputs matter exercises the essential creative agency. The AI, however sophisticated, operates within their design.

This isn't about diminishing AI's capabilities. It's about clarifying what makes human contribution unique. We're moving from competing with AI on execution—a game we'll lose—to focusing on what we've always done best: designing new frameworks for understanding.

Four things matter for attribution in this model:

Architectural intent: Did you deliberately design conditions for discovery?
Cognitive integration: Do you treat AI as extended cognition, not a separate agent?
Epistemic responsibility: Do you take ownership of what emerges?
Pattern recognition: Can you spot which outputs actually matter?

This framework has big implications.

For academia, it means the researcher who designs the discovery architecture gets primary authorship, not the AI that helps execute it. For business, it means IP follows architectural contribution. For creative work, it means the human who sets up the creative framework owns what emerges.

Critics worry this could let people claim credit for AI's work with minimal contribution. But the framework prevents this—genuine architectural contribution requires deep engagement and ongoing responsibility.

Others worry about AI autonomy. But sophistication isn't agency. No matter how capable AI becomes, it operates within human-designed architectures.

The deeper point is that this reframes human uniqueness. Our special capability isn't calculating or pattern-matching or even articulating ideas—all things machines can do. It's asking new questions, imagining new frameworks, designing new architectures for understanding.

By locating human value in architectural imagination rather than operational execution, we create space for both human creativity and AI capability to flourish. We're not retreating from AI but stepping into our actual role: architects of the systems where discovery happens.

This isn't just about attribution. It's about preserving human agency in its highest form—the agency of those who design the stages where knowledge emerges. In the age of AI, the most human act remains what it always was: creating new frameworks for understanding.

As AI gets more capable at execution, human capacity for architectural innovation becomes more, not less, important. We're not competing with AI. We're designing the spaces where intelligence—human or artificial—can discover.