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Springboards is betting that LLM output homogeneity is a real creative liability, and its product Flint, built on a fine-tuned version of Qwen 3, tries to fix it by injecting controlled randomness at specific decision points in a model’s output rather than turning up the temperature globally. Temperature is the dial that controls how predictable or surprising an AI’s word choices are. The target market is advertisers and marketers. Early user Maximilian Weigl, cofounder at marketing firm Uncommon, runs Flint alongside ChatGPT, Claude, and Gemini as a deliberate creative provocation rather than a replacement.
What this means for your business
Marketing and creative teams already running multi-model stacks have been quietly solving for this problem themselves, cycling through ChatGPT, Claude, and Gemini hoping for variation that rarely materializes because all three models are trained toward the same statistically safe center. Flint’s pitch lands hardest for teams whose output is genuinely differentiated by surprise value, brand voice, or conceptual novelty, and sits much further down the priority list for teams where speed and consistency are the actual deliverable.
The technical claim here is worth holding up. Standard temperature adjustment, which just increases randomness across every word a model generates, produces noise as often as it produces creativity. Springboards trained Flint to identify semantically meaningful branch points, the moments where a choice of destination, headline, or concept actually changes the idea, and apply randomness selectively there. Whether that training holds up outside the marketing use case is untested, but the underlying diagnosis is correct. Blunt temperature increases are why “creative mode” on most chatbots just makes responses weirder, not better.
Weigl’s own caveat is the sharpest thing in the piece: nine times out of ten, average is fine. That’s not a knock on Flint, it’s a segmentation signal. If your team’s AI use is dominated by summarization, drafting, and research synthesis, a creativity-boosting tool sitting in the stack adds coordination overhead without a commensurate return. The renewal question worth asking isn’t whether Flint produces more interesting outputs in a demo, it’s what share of your team’s actual AI workflows require creative divergence rather than competent convergence.
Concept deep-dive: Temperature in language models
Temperature controls how a language model picks its next word. At low temperature, the model almost always chooses the statistically most likely word, producing consistent but predictable output. At high temperature, it samples from a wider range of options, like a writer who stops reaching for the obvious word. The business problem is that raising temperature globally increases randomness everywhere, including in places where precision matters, which is why Flint’s targeted approach is the more defensible engineering bet.
Based on reporting from LLMs are stuck in a groupthink groove. This startup is trying to get them out., originally published 2026-07-01 10:35:00.
