Aaron Simpson discusses the monetisation challenges faced by AI systems, particularly conversational assistants. Traditional ad-based monetisation methods do not fit well within AI interactions as they lack a visible UI. Most AI models, built on non-commercial datasets, struggle to integrate monetisation without eroding trust. Though options like subscriptions or pay-per-use models exist, scalability is a concern. Simpson proposes using structured datasets like Kindred's Global Merchant to create direct connections between AI responses and real-world commercial products, allowing monetisation without intrusive ads and maintaining user trust.
As AI tools like ChatGPT evolve, the challenge lies in monetising effectively without compromising user trust, avoiding intrusive ads and ensuring a seamless experience.
The monetisation landscape for AI struggles between outdated ad models and new methods like subscriptions, both failing to satisfy user experience and trust.
Kindred's Global Merchant dataset provides an innovative approach to link AI-generated recommendations directly to commercial outcomes without ads or sponsorships.
This shift towards structured, data-driven monetisation can help AI systems fulfill commercial intent while maintaining transparency and user-centric design.
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