Breaking Free from the “Age of Average” – Why Springboards is Here to Revitalize Creativity
March 1, 2026
Insights
Obviously sharing a deck without the commentary leaves lots of room for misinterpretation but TLDR this is a biased opinion on the NEED for the industry to change the way we operate and to not look at AI as doom but an opportunity for rebirth.
The advertising industry is in a strange place
Somewhere between chasing clicks and feeding the algorithm, something got lost. The creativity that once defined this field—big ideas, bold campaigns, truly different thinking—got buried under “self-serve” ads, popularity contests, and a relentless drive to please the middle. It feels like we’ve entered what some are calling the “Age of Average,” where everything seems tailor-made for safe, widespread appeal but lacks the soul that once made ads unforgettable. The Springboards team? We think it’s high time for a change.
As AI sweeps through the industry, the question isn’t if it’ll change advertising—it already has. The real question is, will AI be a lifeline to help us out of the creative hole we’ve dug? Or will it dig us in deeper? At Springboards, we see a way forward where AI isn’t the enemy but the creative ally we’ve been waiting for. We believe that with the right tools—and a little bravery—the industry can be revitalized, not just to reflect culture but to break it wide open. Here’s a bit of our journey to this vision and why we’re just getting started.
How Did We Get Here? A Quick Diagnosis of the Problem
Our founder, Pip Bingemann, summed it up perfectly at Cannes in Cairns: “AI is ripping apart the ad world, and damn, isn’t it about time?” What he means is that AI isn’t inherently the problem here. Sure, the machines are here, but they’ve always been around in one form or another. The real issue lies in how we’ve used these tools so far, often in ways that limit creativity instead of pushing it forward.
Three major factors have contributed to this stifling of creativity:
Advertising Became Self-Serve: The shift toward digital ads made it possible for everyone to access and create campaigns with minimal expertise. This self-service model may be efficient, but it also means that thoughtful, well-crafted storytelling often takes a back seat to quick, cheap, and easy solutions.
Relevance Over Distinction: The pressure to appeal to the masses has reduced creative expression to whatever happens to be popular right now. As a result, ad content has become predictable, losing the unique edge that makes ads stick.
The Tyranny of Numbers: Metrics rule everything, and we’ve all felt it. When the focus is solely on views, clicks, and conversions, there’s little room left for originality. We’ve become slaves to what the numbers tell us is “successful,” often at the cost of creative risk-taking.
So here we are, with an industry that’s found itself boxed in. Popularity has replaced boldness, and mediocrity is dressed up as success. But just blaming the machines (or the algorithms) is too easy. The truth is, clients, agencies, and even creatives ourselves have all contributed to this mess. But it’s not too late to fix it.
Enter Springboards: A New Way to Think About AI and Creativity
At Springboards, we’re choosing to look at AI differently. Instead of viewing it as a factory for cookie-cutter ideas, we see AI as a way to get back to the heart of creativity. With Springboards, our goal is to design a tool that works with creative humans, not replacearound them. The machines may be powerful, but so is human imagination—and when those two powers come together, the possibilities are huge.
What makes Springboards different is our approach: we don’t just throw in a bunch of features and call it innovation. Instead, we focus on what actually matters for creatives today. We’re building a platform that fuels unique, inspired, and even risky ideas—because the best ideas are often the ones that break the rules. Springboards is designed to help you push past the safe and obvious, bringing bold ideas back into the spotlight. This isn’t another trend-chasing tool; it’s a creative partner with a shared goal—to take creativity to the next level.
Building Momentum: The Early Buzz
Since our beta launch, we’ve seen an amazing response. Industry leaders, like Zoe Scaman, have shared their excitement, and her tweet alone sparked an unexpected wave of interest. It was proof that creatives everywhere are craving something that feels real—a tool that challenges them, pushes them, and ultimately brings more fun back to the work they love. With demo requests piling up and a waitlist growing, we’re more motivated than ever to bring this new creative approach to as many people as we can.
But this is only the beginning. Zoe’s reaction, along with others, shows us that there’s a real hunger for change. Creatives want a tool that works with them, not against them, to shake up the industry norms. Springboards.ai is just that—a fresh way forward, built to make the creative journey exciting, unpredictable, and, most importantly, ours.
What’s Next? The Official Launch and Beyond
The excitement so far is humbling, but we know the real journey is just beginning. With the official launch of Springboards just around the corner, we’re gearing up to make an even bigger impact. We’re not interested in offering yet another “solution” that falls short on creativity. Instead, we’re focused on providing a tool that sparks curiosity, pushes boundaries, and turns the creative process into an adventure again.
This is a call to every creative out there tired of the “Age of Average.” We’re building Springboards for those who believe that creativity can, and should, disrupt, inspire, and even annoy sometimes. If you’re ready for something different, we invite you to join the waitlist and take part in this journey with us.
So here’s to the end of the average and the beginning of something extraordinary. Creativity deserves more, and with Springboards.ai, we’re just getting started.
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.
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].
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.
Figure 1: Top ten tokens predicted by Llama 2 as continuations to "[INST] Give me a number between 1 and 10 [/INST] Sure! The number I am thinking of is…"
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.
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.
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.
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.
[4] Kirk, Robert, et al. "Understanding the effects of RLHF on LLM generalisation and diversity." International Conference on Learning Representations. Vol. 2024. 2024.
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.
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.
As many have sung, “the stars at night are big and bright, deep in the heart of Texas” and let me say, the creative stars were big and bright in Deep Ellum, home to TRG. TRG hosted us for a night of Raging WITH the Machines, where Springboards co-founder and CEO Pip Bingemann opened the audience's eyes to the risks AI can pose to creative thinking and Dustin Ballard, TRG Creative Director and the mind behind There I Ruined It, got the crowd laughing and pondering what AI means for music.
At Springboards, we proudly call ourselves a self-loathing AI company. Not because we hate AI, but because there can be such a negative connotation about it. Certain people with big platforms love to pitch AI as a magic button: run all your campaigns, replace strategists, no need for production or media teams. And that just isn’t what we believe. Yes, Springboards uses artificial intelligence, but it really only comes to life when human intelligence is in the driver's seat, pushing back and steering it.
TRG’s Chief Technology Officer, Randy Bradshaw, spoke of the importance of keeping “humans in the loop”. Due to the nature of today’s industry, agencies don’t always have time to experiment and play, however Randy shared that AI tools allow them to fail faster, which in turn allows them to learn faster and iterate quicker. Randy and Pip both hit the same key point: using AI comes with responsibility. We need to bring our critical thinking, lived experiences, and the knowledge of what is happening in the world around us, to whatever AI tool we are using.
Pip shared research from Springboards we've found again and again: traditional LLMs are so good at bringing everyone to the same place (for example, they love recommending pepperoni as a pizza topping - what about eggs? Or pineapple?!).
Which is exactly what marketers need to watch out for.
He challenged the audience to input their favourite song into an LLM and ask it to “make it better”. Notice what happens. It sands off the edges. It will strip the emotion, the story telling, the rage, from the song. Who wants a dull song? Not me.
Then Dustin took the stage and there certainly were no boring songs (ps if you aren’t following There I Ruined It, you need to). He reprised his well known Ted Talk, “Is AI Ruining Music”, (yes, he confirmed Sir Richard Dawkins was in the audience listening to Baby Got Back) and challenged us to think about what music truly is. Much like the synthesiser was criticised when it was used in popular music, the question now is: are musicians still “musicians” if they use AI?
Dustin’s takeaways were simple and to the point: consider the intent (is it additive or just more content to try to increase steam counts), is the artist trying to be deceptive about the use of AI, and then consider what the original musician might think. There are ways to leverage AI in music, you just need to be responsible about it.
The night wasn’t just all talk though. The whole crowd helped rage with the machines as we sparked campaign ideas for the Deep Ellum neighbourhood of Dallas. Deep Ellum has a rich history of music and culture, but is struggling due to major infrastructure projects. Stores are closing and foot traffic is dying down. So we worked together as a group to spark some ideas of how we could hype up the neighbourhood during this challenging time. From celebrating the grit, to scavenger hunts finding the vibrant murals around the neighborhood, all the way to robotic shoes that help you explore the history of Deep Ellum - the ideas were flowing. Ideas sparked with AI and brought to life by the people.
In the end, we all agreed, AI can, and sometimes, should be used to increase creativity - so long as humans are in the loop, of course. So let’s rage on!
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