People really hate AI, so why can’t they get enough?
What changed
AI users express a strong contradiction. On one side, there is deep skepticism and frustration with how mainstream models handle responses, often defaulting to safe or repetitive answers. On the other side, those same users become hooked on AI’s potential and immediate accessibility. Springboards, a startup developing a large language model (LLM), directly confronts this tension. Their CEO describes their AI as “self-loathing” because it aims to break out of bland response patterns and offer more diverse, nuanced outputs.
Why builders should care
The appetite for AI is not just about raw capability but about variety and authenticity in interactions. Mainstream LLMs tend to prioritize accuracy and safety at the cost of creativity and nuance. This limitation frustrates experienced users and reduces AI’s appeal for complex tasks that require flexible thinking or ideation. Startups like Springboards are betting on expanding the types and tones of AI-generated content, tackling a key pain point that could differentiate their technology in a saturated market.
The practical takeaway
For developers and founders building AI products, this is a clear signal: AI success depends not only on correctness and reliability but on offering richer and more engaging user experiences. Models that simply avoid mistakes but produce predictable outputs risk user disengagement. Delivering a wider variety of plausible answers—while still managing risks—can make AI tools more valuable in applications like brainstorming, content creation, and customer engagement. It also pressures existing AI providers to rethink how they balance safety and creativity.
What to watch next
Observe how startups and established AI vendors experiment with response diversity and persona customization. The companies pushing the limits of AI’s “voice” may set new expectations for user experience. Investors and operators should track whether these differentiation efforts translate into higher engagement metrics or market traction. Also watch regulatory or safety frameworks reacting to AI’s growing unpredictability as variety rises in outputs.
AI Quick Briefs Editorial Desk