LLMs are stuck in a groupthink rut. This startup is trying to get them out.
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Most large language models exhibit a striking homogeneity in their outputs — ask any major chatbot for a random number and you'll almost always get 7. Research presented at NeurIPS 2025 confirmed this 'artificial hivemind' effect, showing that 25 different LLMs converged on nearly identical metaphors when given open-ended prompts. Australian startup Springboards built Flint, an LLM fine-tuned on Alibaba's Qwen 3, that targets specific decision points in its output to inject controlled randomness rather than blindly cranking up the temperature parameter. The approach aims to serve creative professionals in advertising and marketing who need genuinely novel brainstorming rather than statistically average responses.
