AI Philosophy
AI, Art, Creativity, and Intentionality: What Machines Cannot Choose
AI can generate outputs that look creative. These three pieces ask whether that matters — and what we should build instead.
Why A.I. Isn’t Going to Make Art
TLDR: Ted Chiang argues art requires intentional choices shaped by lived experience and personal meaning. AI outputs lack this intentionality entirely. The appearance of art is not art.
Key Insight: Art is defined by the human choices behind it, not the quality of the output.
The Problem with AI That’s Too Human
TLDR: We are in the “horseless carriage” era of AI — mapping it onto existing human roles instead of imagining AI-native applications with no pre-AI parallel. This limits both the technology and our thinking.
Key Insight: Stop asking what job AI can replace; ask what new capability it makes possible.
5 New Thinking Styles for Working with Thinking Machines
TLDR: AI demands a cognitive shift away from the rationalist paradigm toward one centered on sequences and engineering outcomes. The authors outline five mental models for productive human-AI collaboration.
Key Insight: The bottleneck to AI productivity is the user’s ability to think in new ways.
What does this mean for how we think about AI?
Creativity is not output quality — it is intentional choice under constraint. AI changes the creative toolkit but not the locus of meaning, which remains human. The practitioners who thrive will be those who develop new cognitive frameworks for collaboration rather than treating AI as a faster version of themselves.