The creative process with AI involves exploring many options, most of which are imperfect. This makes the collaboration a version control problem. Users need tools to easily branch, suggest, review, and merge ideas, much like developers use Git, to manage the AI's prolific but often flawed output.

AI errors, or "hallucinations," are analogous to a child's endearing mistakes, like saying "direction" instead of "construction." This reframes flaws not as failures but as a temporary, creative part of a model's development that will disappear as the technology matures.

Like chess players who still compete despite AI's dominance, humans will continue practicing skills like writing or design even when AI is better. The fear that AI will make human skill obsolete misses the point. The intrinsic motivation comes from the journey of improvement and the act of creation itself.

Product leaders must personally engage with AI development. Direct experience reveals unique, non-human failure modes. Unlike a human developer who learns from mistakes, an AI can cheerfully and repeatedly make the same error—a critical insight for managing AI projects and team workflow.

True creative mastery emerges from an unpredictable human process. AI can generate options quickly but bypasses this journey, losing the potential for inexplicable, last-minute genius that defines truly great work. It optimizes for speed at the cost of brilliance.

AI's occasional errors ('hallucinations') should be understood as a characteristic of a new, creative type of computer, not a simple flaw. Users must work with it as they would a talented but fallible human: leveraging its creativity while tolerating its occasional incorrectness and using its capacity for self-critique.

The most creative use of AI isn't a single-shot generation. It's a continuous feedback loop. Designers should treat AI outputs as intermediate "throughputs"—artifacts to be edited in traditional tools and then fed back into the AI model as new inputs. This iterative remixing process is where happy accidents and true innovation occur.

The real danger of new technology is not the tool itself, but our willingness to let it make us lazy. By outsourcing thinking and accepting "good enough" from AI, we risk atrophying our own creative muscles and problem-solving skills.

Instead of forcing AI to be as deterministic as traditional code, we should embrace its "squishy" nature. Humans have deep-seated biological and social models for dealing with unpredictable, human-like agents, making these systems more intuitive to interact with than rigid software.