The tendency for AI models to "make things up," often criticized as hallucination, is functionally the same as creativity. This trait makes computers valuable partners for the first time in domains like art, brainstorming, and entertainment, which were previously inaccessible to hyper-literal machines.

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Demis Hassabis likens current AI models to someone blurting out the first thought they have. To combat hallucinations, models must develop a capacity for 'thinking'—pausing to re-evaluate and check their intended output before delivering it. This reflective step is crucial for achieving true reasoning and reliability.

Today's dominant AI tools like ChatGPT are perceived as productivity aids, akin to "homework helpers." The next multi-billion dollar opportunity is in creating the go-to AI for fun, creativity, and entertainment—the app people use when they're not working. This untapped market focuses on user expression and play.

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.

Finding transformative AI use cases requires more than strategic planning; it needs unstructured, creative "play." Just as a musician learns by jamming, teams build intuition and discover novel applications by experimenting with AI tools without a predefined outcome, letting their minds make new connections.

Early AI agents are unreliable and behave in non-human ways. Framing them as "virtual collaborators" sets them up for failure. A creative metaphor, like "fairies," correctly frames them as non-human entities with unique powers and flaws. This manages expectations and unlocks a rich vein of product ideas based on the metaphor's lore.

The critique that LLMs lack true creativity because they only recombine and predict existing data is challenged by the observation that human creativity, particularly in branding and marketing, often operates on the exact same principles. The process involves combining existing concepts in novel ways to feel fresh, much like an LLM.

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.

While AI offers efficiency gains, its true marketing potential is as a collaborative partner. This "designed intelligence" approach uses AI for scale and data processing, freeing humans for creativity, connection, and building empathetic customer experiences, thus amplifying human imagination rather than just automating tasks.

The debate over AI's 'true' creativity is misplaced. Most human innovation isn't a singular breakthrough but a remix of prior work. Since generational geniuses are exceptionally rare, AI only needs to match the innovative capacity of the other 99.9% of humanity to be transformative.