Large language models have a well-documented tendency to converge on the safest, most statistically probable answer. Ask almost any chatbot for travel recommendations, marketing copy, or creative concepts, and the output will be competent, coherent, and often indistinguishable from what every other user receives. Springboards, a company building AI tools for advertisers and marketers, recognized this problem not as a flaw in the underlying technology but as a failure of implementation. Its answer is Flint, a version of Alibaba’s Qwen 3 model that introduces randomness at precisely the right moments rather than scattering it uniformly across every token. The result is a system designed to break the groupthink that plagues most chatbot interactions without sacrificing the consistency users depend on for everyday tasks.
The Problem with Blanket Randomness
Standard large language models expose a single parameter—usually called temperature—that controls the randomness of the entire output. Turning it up makes every word more surprising, but it also makes the model less reliable. Turn it down and the output becomes predictable and repetitive. Springboards concluded that this blunt approach makes no sense for creative tasks. Dialing up randomness across every word in a response does not create useful variety; it creates chaos. What advertisers, marketers, and anyone seeking fresh ideas actually need is a model that stays predictable for most of its output and only introduces variation at decision points where multiple valid answers exist.
The canonical example is a travel query. When a user asks, “Where should I go in Europe?” the model only needs to inject randomness just before it names a destination. Every other word in the response—the grammar, the reasoning, the polite framing—should remain stable. Flint is engineered to identify those specific junctures in its own output where multiple alternatives are equally valid and to fill those slots with words or phrases drawn from a slightly more random distribution.
How Flint Achieves Targeted Randomness
Springboards trained its version of Qwen 3 to recognize these branching points during generation. Rather than altering the model’s architecture in a fundamental way, the team fine-tuned it to detect moments in a sequence where the next token could plausibly be one of several candidates without breaking coherence. At those points, Flint deliberately selects a less probable token than the base model would choose. The result is an output that remains grammatically sound and contextually appropriate but contains an element of surprise exactly where it matters.
This is a fundamentally different approach from the “creative mode” toggles that many AI writing tools offer. Those sliders apply a uniform change to the sampling strategy for every token. Flint applies a surgical change only where variety serves a purpose. The rest of the response follows the model’s standard, high-probability distribution.
Maximilian Weigl, cofounder and chief strategy officer at the marketing firm Uncommon, describes the effect as an invitation to think wider. Weigl’s team runs Flint alongside ChatGPT, Claude, and Gemini, and he notes that the tool is programmed to throw in an oddball when the situation calls for it. That oddball, he says, is precisely what makes the difference between output that feels derivative and output that sparks a real idea.
The Real-World Trade-Off: Average Is Often Enough
Weigl is also clear about where Flint fits into a professional workflow. Nine times out of ten, he says, the average output from a standard model is perfectly fine. Most people and most tasks do not require a boundary-breaking suggestion. They need a competent, mass-market answer that feels familiar and reliable. Flint is not a replacement for ChatGPT or Claude in the majority of use cases. It is a tool for the tenth use case where the ordinary answer is not good enough.
That distinction matters because it prevents Flint from being oversold as a universal solution. Springboards is positioning it for advertisers and marketers—its existing customer base—where the difference between a campaign built on expected language and one built on a genuinely surprising angle can be measured in engagement and conversion rates. For those users, the selective randomness that Flint provides is not a gimmick. It is a functional requirement.
Cautions from the Field: Over-Reliance on Any AI
Weigl also offers a caution that applies to Flint and every other language model on the market. Relying too heavily on AI output, regardless of how creatively it is tuned, risks eroding the very human judgment that produces original work. He is direct about this: if he saw a member of his team copy-pasting content directly from any AI, he would tell them that is not their job. Thinking, talking to other people, and using one’s own voice remain the core skills that no model can replace.
This perspective is worth noting because it frames Flint not as a silver bullet for creativity but as a complement to human ideation. The tool introduces variety. The human decides whether that variety is useful. Springboards cofounders Bingemann and Browne echo this sentiment, arguing that the real value of Flint is giving people a choice about the direction their output takes. Let the machines handle the bulk of the work, but leave the creative judgment to the person holding the keyboard.
Who Flint Is For
Flint is currently aimed at advertisers and marketers because those are the customers Springboards already serves. But the underlying insight—that lack of variety is a problem for anyone using chatbots—is general. Anyone who has ever prompted a model for ideas and received the same three suggestions every time has felt the limitation that Flint is designed to address. The product is not yet available as a general-purpose consumer tool, but the direction is clear: targeted randomness as a feature, not a bug.
For now, the practical path for most readers is to evaluate their own workflows. If the majority of your AI interactions are transactional—summarizing, drafting standard emails, generating boilerplate code—Flint’s approach offers little advantage. But if you regularly find yourself asking a model for creative options and receiving answers that feel recycled, the concept behind Flint is worth watching. The idea of tuning randomness at the point of decision rather than across the entire output is one that other model providers are likely to adopt.
What This Means for the Next Wave of AI Tools
Springboards’s approach with Flint signals a maturation in how the industry thinks about model behavior. The default assumption has been that a single randomness slider is sufficient for all use cases. That assumption is breaking down as users demand more nuanced control over outputs without having to understand the underlying sampling mechanics. Flint’s targeted randomness is one early example of what that future might look like: models that know when to be surprising and when to be predictable, and that give the user the final say.
Bingemann puts it simply: Variety is great when you are trying to spark ideas. The alternative is letting the machines do it all and ending up in a gray, boring world. Flint is a small but deliberate step in the other direction. For any professional who has felt the limits of LLM groupthink, it is a development worth monitoring closely.
