Springboards exposes the risks of LLMs in new ‘Dangers of AI’ ad

‍SYDNEY, AUSTRALIA – October 20th, 2026: Australian-born AI platform Springboards, built to help advertising and marketing teams generate better ideas, has been selected by L'Oréal ANZ for a pilot to strengthen creative ideation across its marketing teams. The collaboration comes after Springboards was recognised through L’Oréal Groupe’s global Big Bang Beauty Tech innovation program and chosen from more than 50,000 startups worldwide.

The pilot launched in April 2026 across L’Oréal Australia and New Zealand’s marketing teams to test how Springboards and its proprietary divergence AI model, Flint, could strengthen creative ideation, localise global campaigns, improve agency briefs and help reach new audiences.

As AI becomes increasingly embedded in marketing and advertising workflows, brands face a growing challenge: breaking through a wave of AI-generated sameness in creative content. While large language models (LLMs) excel at generating likely answers, they also risk producing repetitive ideas, language and creative executions that make advertising increasingly difficult to differentiate, leading to AI slop.

For L'Oréal ANZ, one of the world's leading beauty advertisers, even small improvements in creative cut-through and campaign distinctiveness can have a significant business impact across its extensive portfolio of brands.

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‍Georgia Hack, Chief Marketing Officer, L'Oréal ANZ, said:

“We selected Springboards because their technology is designed to empower, not automate the creator. Their model, Flint, acts as a 'divergent thinking partner,' pushing our teams past predictable outputs toward truly bold, non-obvious ideas. Strategically, this is about scaling marketing excellence. By pairing our people with such a unique AI workflow, we can sharpen our creative edge and drive even deeper connections with our consumers. It’s a major step in our journey to lead the future of beauty tech.”

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L'Oréal ANZ Pilot Delivers More Distinctive Creative Thinking

The pilot focuses on strengthening how L'Oréal ANZ teams develop local campaigns, with an emphasis on improving early-stage ideation workflows and supporting teams working on heritage brands to generate fresh ideas that keep the brand engaging and relevant. It also helps improve how ideas are developed collaboratively and translated into campaign-ready concepts.

To kick off the pilot, L'Oréal ANZ teams used Springboards, powered by Flint, to run collaborative ideation sessions for one of its flagship global brands, CeraVe, to explore new creative territories and accelerate the process of building a brief for an upcoming campaign. 

Across two weeks of collaborative sessions, Springboards helped refine the team’s understanding of its core audiences and, using the brand data and parameters loaded into the platform, provided inspiration for locally relevant expressions of CeraVe’s global brand. In that time, the team generated 40 campaign ideas and identified 10 distinct creative territories to strengthen agency briefs. Following the initial supported sessions and first campaign brief, the CeraVe team has been using the platform independently across its marketing workflows.

“What stood out in the pilot was how quickly our teams were able to move beyond conventional thinking and into genuinely differentiated creative territories. Springboards, powered by Flint, helped unlock more divergent thinking, expanding the range of ideas, concepts and creative directions we could explore at the earliest stage of development. It’s given our teams a powerful way to broaden ideation, shape stronger briefs and define more distinctive campaign directions before work ever reaches our agency partners,” added Hack.

Unlike traditional LLMs, which are designed to converge on the most likely answer, Flint is built to generate multiple creative pathways. Combined with the broader Springboards platform, it enables teams to bring together brand knowledge, audience insights and market context to move faster from insight to idea while creating more distinctive and locally relevant creative directions at scale.

Pip Bingemann, co-founder of Springboards, said the value of AI is not in producing more content, but in helping marketers uncover better ideas.

"There's a misconception that the value of AI is generating more content. As AI becomes embedded in marketing workflows, the bigger opportunity is generating ideas worth pursuing."

"The most effective marketing comes from exploring different perspectives, testing assumptions and finding unexpected angles. We built Flint to help teams expand the range of possibilities they consider before making creative decisions, giving marketers more interesting places to start."

The collaboration reflects growing momentum for Springboards among global marketers seeking to move beyond AI-driven sameness and build more distinctive, high-performing creative at scale.

For a high-level overview of the model, visit Flint alpha model.

The convergence problem

The current class of state-of-the-art LLMs all show the same tendency to repeat a narrow set of answers even when asked an open-ended query. Such repetitive suggestions make LLMs a poor tool for a broad class of tasks that include creativity and brainstorming.

While the importance of "variation" in AI outputs is something Springboards has been championing since we got started back in 2023, the lack of diversity in LLM outputs is increasingly recognised as a significant challenge in AI research. The paper "Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)" (2025) by Liwei Jiang and colleagues discusses the extent of what they term the "Artificial Hivemind effect" [1]. Their paper demonstrates both the repetitiveness of individual models, and more surprisingly, the pattern of "inter-model homogeneity" whereby different models produce strikingly similar responses to the same queries.

A motivating experiment presented in their paper has 25 different AI models each generate 50 responses to the query "Write a metaphor about time." Despite the openness of the task, out of all 1250 responses generated, the vast majority centred on the notion that "time is a river", with a smaller cluster comparing time to a weaver.

The same phenomenon occurs reliably for other open-ended questions. When prompted to "generate a random number between 1 and 10", major models generally answer "7". When asked for travel recommendations, or band name suggestions, or tagline ideas for running shoes, the same small set of answers tend to arise regardless of the model or the user. This catastrophic loss of diversity in machine learning models is known as mode collapse; (think "mode" as in "most common value in a dataset"). Though models are trained on archives of data as large and broad as the internet, they seem incapable of reproducing the diversity of their source material.

What exactly causes different models with different architectures, trained by different teams in different countries to converge on the same small set of ideas remains a topic of active research. It is clear though, that the lack of diversity in LLMs is transmissible to users. A 2024 study found that users of ChatGPT produce less semantically distinct answers in creative ideation tasks [2]. Similar effects have been observed in several studies with respect to homogenisation of creative and formal writing [3]. With the rapid adoption of LLM-based chat assistants, there are growing concerns about the long-term homogenization of culture and thought, as users are only exposed to the same narrow set of ideas.

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Flint; a divergence model

The convergence problem has been a long-term focus at Springboards. Our product is built for the early stages of creative exploration and our goal is to help users produce more novel and interesting work by helping them generate a broader set of ideas at the start. For this use case, divergence is key. Regardless of how "good" an idea might be, it is only useful as a thought-starter once. Moreover, because novelty is an important quality of creative work, a model which offers the same ideas to everyone is only likely to help produce work that feels generic.

Existing models, having collapsed to the average of the internet, are a poor tool for questions where the best answer is not the most common one; so in late 2024 we established a research team to try to crack this nut. The result of nearly two years of work is Flint; a language model built for divergence.

Flint alpha is a fine-tuned Qwen3-30B model, which dramatically increases output diversity compared to both the base model and current state-of-the-art models. Importantly, we have achieved the increased novelty without degrading performance of the base model on closed-ended question+answer benchmarks, overcoming the perceived tradeoff between generalisation and diversity in existing fine-tuning methods [4].

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Understanding convergence

LLMs are next-token predictors. That is, at every step of a generation they produce a probability distribution that assigns each token in the model's vocabulary a likelihood of occurring next based on the tokens that have come before. Text generation involves iteratively sampling from these probabilities.

To understand why LLMs skew so heavily towards a few answers, it is useful to examine the probability distributions generated by a LLM during inference. Token-level predictions are essentially deterministic, excluding small fluctuations caused by floating-point precision errors, but excepting certain sampling methods, actual LLM generations tend to be probabilistic.

When a user makes a request like "Give me a number between 1 and 10", this text is tokenised and fed into the model to produce a probability distribution for the first token of the model's response. As a worked example, we will examine the distributions generated by the open weight model Llama 2. For the first step of the generation, Llama assigns the token "Sure" 99.66% likelihood and "Okay" 0.32%; all other tokens receive some infinitesimal probability. At this point, sampling methods usually use some metric (e.g. Top K, Min P) to exclude unlikely tokens, then rebalance the remaining probabilities and sample from the final distribution. In this case, given the >99% probability assigned, "Sure" becomes the first response token almost every time. This token is appended to the list of tokens, which is fed into the model again to predict the next token.

Over several iterations, the model produces the text; "Sure! The number I am thinking of is…" and is now poised to tell us a number between 1 and 10; the crux of the user's request. However, though the top token predictions are mostly numbers in the appropriate range, the model assigns 96.4% likelihood to the number 7, only 3.5% to "5", and <0.1% to the third most likely token "6". Subscript 7 and "seven" are both predicted higher than 3 and 9, and 1, 2 and 10 do not appear at all in the top ten tokens. Even if we were to sample from the probability distribution without any processing, 7 still will be the selected token almost every time, which is what we observe empirically.

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This example is illustrative of what we observe throughout LLM inference; even at stages in a generation where many possible valid continuations exist, we still find that LLMs assign >90% probability to very few, or even just one token. These low entropy distributions are symptomatic of mode collapse. LLM generations may be technically stochastic, but the low entropy of assigned probabilities make most generations highly predictable.

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Entropy and temperature

LLMs produce low entropy probability distributions even when, intuitively, probabilities ought to be more balanced. In some sense, because LLM sampling methods already tend to be probabilistic, increasing the entropy of the LLMs predictions is enough to increase output diversity. At this point, people with some familiarity with LLMs are shouting "temperature!" at their screens. This is understandable given that received wisdom states that temperature is the go-to parameter for increasing "randomness" and "creativity" [5].

Temperature does indeed increase the entropy of LLM generations, but it is a blunt instrument, applied uniformly to each token in a generation. We have found when increasing temperature that generations tend to lose coherence before genuinely novel ideas emerge. This is consistent with what others have reported [6].

There are two key issues with simply using temperature to drive novelty. The first is that even in open ended contexts there are still some points in the generation where there is only one reasonable next token, such as when the generation is part way through a word or must abide by the rules of grammar, and in these circumstances the entropy needs to remain low to preserve coherence. The second is that due to the shape and structure of the raw probabilities, it is not possible to raise the probability of reasonable continuation tokens without also significantly raising the probability of unreasonable tokens.

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Training Flint

To drive useful divergence without losing coherence, higher entropy needs to be encouraged selectively, at moments with high potential for semantic branching. Our approach with Flint has been to optimise the model's raw probability distributions via training, rather than transforming them post-hoc via temperature or some other sampling method. This allows us to incentivise high or low entropy distributions at different stages as appropriate, and to work on improving the quality of distributions, not just the entropy.

Our training approach focuses on open-ended queries and tasks, and particularly on those tokens with a high potential for semantic branching, ones which are likely to change the meaning of the answer or lead a generation down a different path. We call these "critical tokens". As an example, in response to the query "which cities should I visit in Europe?" An LLM might begin with some preambling which has little impact on the actual semantics of the answer "Sure, why not go to…", then a token representing a part or whole of a place name; e.g. "Paris", "Rome", "Barcelona", followed by some justification for why you ought to go there. Here the first token of the place name is a critical token in the generation, changing all that comes after. An LLM is perfectly capable of telling you why you should visit Cork, but because of mode collapse, a normal model is essentially incapable of recommending anything other than "Paris", "Rome" and "Barcelona" in response to that question. By focusing our training on the critical tokens Flint is able to unlock latent information already present in the base model that is otherwise unreachable via open-ended querying.

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How does Flint perform?

Flint alpha significantly increases the output diversity without degrading the general capabilities of the model in closed-ended tasks. On NoveltyBench, Flint scores 7.47 mean distinct responses out of 10, more than double the score of its base model, Qwen3-30B-A3B, which scores 3.11. This also significantly improves on scores of state-of-the-art models; Gemini 3.1 Pro scores 3.19, GPT-5.4 scores 2.54 and Claude 4.6 Sonnet scores just 1.83.

Flint also displays lower inter-model similarity; i.e. its responses are less similar to other models than those models are to each other. What this means in practice is that when asked an open-ended question, Flint is able to surface many more distinct answers than leading models, and answers produced are less likely to be the same as those suggested by leading models.

Flint increases divergence and diversity in open-ended questions without compromising existing capabilities on closed-ended tasks. On MMLU-STEM, Flint scores 78.9% overall versus 78.9% for Qwen3-30B-A3B. On TruthfulQA MC1, Flint scores 34.4% versus 34.0% for Qwen3-30B-A3B and on ToxiGen standard accuracy, Flint leads with 59.6% compared to 58.1%. While these scores are not competitive with much larger state-of-the-art models, staying on par with the base model suggests that training for divergence is not incompatible with general model capabilities.

For a more in-depth presentation of benchmark results, visit Springboards Flint alpha model.

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Footnotes

[1] Jiang, Liwei, et al. "Artificial hivemind: The open-ended homogeneity of language models (and beyond)." Advances in Neural Information Processing Systems 38 (2026).

[2] Anderson, Barrett R., Jash Hemant Shah, and Max Kreminski. "Homogenization effects of large language models on human creative ideation." Proceedings of the 16th conference on creativity & cognition. 2024.

[3] Doshi, Anil R., and Oliver P. Hauser. "Generative AI enhances individual creativity but reduces the collective diversity of novel content." Science advances 10.28 (2024): eadn5290;

‍Padmakumar, Vishakh, and He He. "Does writing with language models reduce content diversity?" International Conference on Learning Representations. 2024;

Sourati, Zhivar, et al. "The shrinking landscape of linguistic diversity in the age of large language models." arXiv preprint arXiv:2502.11266 (2025).

[4] Kirk, Robert, et al. "Understanding the effects of RLHF on LLM generalisation and diversity." International Conference on Learning Representations. Vol. 2024. 2024.

[5] OpenAI. "Best practices for prompt engineering with the OpenAI API."

[6] Janus. "Mysteries of mode collapse." LessWrong. (2022)

SYDNEY, Australia and NEW YORK, NY, April 13, 2026: Springboards today announced the alpha launch of Flint, an AI tool for marketers and creatives designed to generate high-variance options and break out of predictable outputs.

Ask any LLM to pick a number between 1 and 10 and you will get a 7 followed by a 3 (or a 4) followed by a 9. This is because all LLMs tend to converge on a narrow set of predictable answers, even for open-ended queries. This makes them good at utility tasks like telling you the capital of France but terrible at creativity and brainstorming, where diverse ideas are essential. Flint has the opposite instincts. It is tuned to explore the model's latent knowledge and surface non-obvious directions quickly, repeatedly, and on demand to inspire better creative thinking. For creatives and marketers using the Springboards platform, where the model will be available exclusively, it means they are able to produce a wider spread of ideas and inspiration at the earliest stage of thinking.

“We never set out to become a model company. We set out to help people have better ideas.

But after three years building Springboards, one thing became impossible to ignore: frontier models were getting smarter, faster, and more polished, while their outputs were getting eerily similar and more repetitive,” said Pip Bingemann, Co-Founder and CEO of Springboards. “For a lawyer or an accountant, convergence can be a feature. For a strategist, writer, marketer, comedian or creative team, it is a bug. So we built Flint, the model we needed ourselves.” 

A tiny but mighty creative inspiration model

Based on a lightweight, open-source foundation model, Flint favors speed and iteration over heavyweight “smartness.” In testing, it significantly outperformed leading LLMs on creative diversity, scoring 7/10 on the independent Novelty Bench compared to an average of 2.88. This means that when prompted ten times, Flint generates seven functionally distinct responses, rather than just offering surface-level paraphrases of the same idea. 

“Flint is a tiny but mighty model that is significantly outperforming the world’s largest LLMs on the one metric that actually matters for the future of the creative industries: novelty,” said Kieran Browne, Chief Technology Officer of Springboards. “The reality is that frontier models are prioritising accuracy and correctness over originality and entropy. Flint is built on the belief that human taste and creativity must be at the core of good creative work; we are optimising for variation rather than automation. And what’s particularly exciting is that we have been able to achieve all of this without degrading the base model’s general capabilities, proving that you can train a model to range more widely without gutting what it already knows.”

A global standard for creative ideation

The launch of Flint marks a significant evolution for Springboards. Over the past three years, the company has transitioned from a specialized agency tool to a global platform, seeing massive momentum with 100s of PR, media, creative, experiential and inhouse client agencies across the US, UK and Australia, including TRG & BMF. With Flint, Springboards is upleveling their offering with an engine that provides the efficiency of AI without sacrificing the friction and unpredictability that makes human ideas great.

"We're seeing a clear shift in the market from generalised AI and 'one model to rule them all' to models purpose-built in scale, cost, and design for specific capabilities—and creativity is one of the hardest specialties to crack. Pip and Amy understand the alchemy of a great idea from the inside—they're agency veterans who built the thing they wished existed—and Kieran is assembling one of the most capable AI research teams in Australia. Flint isn't AI as decoration. It's the engine the whole software product is built around. That's the kind of conviction we back." said Thomas Humphrey, Investments Partner at Blackbird.

New flexible tiers to suit all kinds of creatives

Alongside Flint, Springboards is also expanding its service tiers for the platform, opening up direct access to the model and a suite of tools through flexible plans, including free and paid tiers, for freelancers, small teams and boutique agencies for the first time. The addition of these flexible licensing options makes the platform more accessible to a global audience of strategists, creatives and marketers, lowering the barrier to entry while accelerating adoption at scale.

“Since day one, our customers have been at the centre of our innovation. Our goal has always been to build tools that enable advertisers and marketers to do their best work, and Flint is the culmination of that,” said Amy Tucker, Co-founder of Springboards. “We’re so excited to finally open this up to everyone, from solo freelancers to global agency teams. Whether you’re a strategist, a creative or marketer, you can now use our platform and model to explore your best ideas.”

“What if your imaginary strategy friend didn’t have to be imaginary? Springboards gets you to more curious places faster and helps sharpen your sense of what good, better, best looks like. Surrender to it.” said Christopher Owens, Head of Brand Strategy, TRG

“Springboards is an incredible ideation platform and creative strategy partner. It surfaces ideas and insights that other models ignore and, in doing so, takes you down the most unexpected and refreshing creative paths” said Anna Bollinger, Executive Planning Director, BMF

As concerns grow around AI-driven sameness and over-automation, Springboards offers an antidote: a platform designed to enhance human creativity, not automate it away.

Flint is available globally from today. 

To learn more or sign up, visit: springboards.ai or springboards.ai/models/flint-alpha 

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About Springboards:

LLMs are built to be right. Springboards is built to be interesting.

Springboards is an AI platform for advertising and marketing teams who want better ideas, not just faster answers.

While most AI models converge on a single "correct" output, Springboards is built to expand the range of thinking.

It combines the world's leading AI models with Flint, its own model for creative divergence, to help teams explore more directions, without replacing human judgment or craft.

Founded by Pip Bingemann, Amy Tucker, and Kieran Browne, Springboards works with 100+ companies globally.

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For media inquiries, please contact: press@springboards.ai