From Agency Life to AI Startup: Pip & Amy’s Origin Story on High Flyers Podcast
March 9, 2026
Events
A fresh look at our co-founders, Pip & Amy's origin story and how they turned redundancy & AI into Springboards. An AI platform built for agencies, by agency vets.
If you thought you knew everything about Pip and Amy's origin story, think again. We learned a ton from Vidit’s “meticulously researched biography” episode of High Flyers Podcast, featuring Springboards co-founders Pip and Amy.
In episode #218 of Vidit’s High Flyers Podcast, Pip and Amy got into some early details of their careers from meeting at a media agency (where Pip literally interviewed Amy for her first job) to building a global AI startup with two small kids, a dark sense of humor, and a shared belief that work should feel like play.
So what do you get when you mix: two agency vets with a ton of experience in creative and media shops, a redundancy or two, two small kids, and a belief that creativity is going to win out no matter how fast AI moves? You get Springboards.
This episode dives into the full journey of agency life, the chaos and challenge of startup building and lots of insights they’ve learned along the way.
Pip is a big fan of being really good at what you’re doing right now and continuing to say yes to get doors to open for you. You’ve gotta keep evolving and learning and being open because you never know where that could lead you.
Amy’s thought is that people often try to specialize too early. You don’t need to have one job for the rest of your life. And as Vidit added, he’s discovered his passion through trying different things. You can’t sit around waiting for your passion to find you.
Listen in for some laughs, some insights on just taking the leap when building your product, and proof that creativity isn’t dying, the value is only going up. It’s on humans to evaluate what great creativity looks like and weed through the average content that so much of AI spits out.
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!
Recently our team ran an experiment to see how quickly we could go from concept to finished work. The experiment started inside our own creative process. We were playing in Springboards and explored nearly 20 different ways to talk about our brand. But we kept coming back to the elephant in the room – that these models are all giving people the same answers.
We wanted to lean into this and decided to dramatise the problem instead of explaining it.
We picked an ad, a recent spot from OpenAI, and flipped the ending to make a point about what happens when everyone uses the same tools – they end up getting sent to the same destination both in real life and creatively.
Our original thought was to see how quickly we could conceptualise this approach and to mock up the concept. What we didn’t expect was how quickly the work would become uncomfortably close to the original.
The acceleration of LLM adoption across marketing and creative industries over the past couple of years has been remarkable. These tools are being woven into workflows everywhere – from concepting to copywriting to production.
When deployed thoughtfully, generative AI can push creative boundaries and help teams explore territory they may never reach by themselves, or help to short-circuit work that could take days or weeks in that creative exploration to help teams move more quickly.
But LLMs are converging – and not enough of us are paying attention.
Recent research from MIT and other institutions — published as the “Artificial Hivemind” study — documents something many of us have felt but struggled to quantify: these models are gravitating toward remarkably similar outputs, even in open-ended scenarios where countless valid answers should exist.
The simple test is just to ask your LLM to generate a random number between one and 10. With 95%+ accuracy you will get a seven every single time regardless of the model, where you live or your chat history. And while there are parallels with humans who also pick seven the most often, at 28%, of the time, LLMs are amplifying the average – from 28% probability to 98%. Doesn’t that tell you everything you need to know?
This isn’t about people using the technology incorrectly. It’s about how the models themselves are designed. They’re trained on patterns and they optimise for coherence and probability. They deliver what’s most likely, not what’s most interesting or unexpected.
And when everyone’s drawing from the same well, standing out becomes exponentially harder.
Which brings me back to our experiment.
Firstly, credit should go where it’s due — our production partners absolutely nailed the brief. Frame composition, lighting, movement – all of it was eerily accurate. Too accurate.
The result raised immediate questions about likeness, intellectual property and how effortlessly these systems can blur ethical lines without anyone deliberately trying to.
We wanted to get close to the original and the technology made it almost effortless to get there.
It crystallised the convergence problem in a way that felt impossible to ignore. If we could recreate a high-production advertisement this accurately, this quickly, with relatively little iteration – what does that mean for originality across the board? What does that mean for our craft? And how busy are copyright lawyers (or their bots) going to be in the years to come?
So we kept playing and pulled the work right back into a safer zone. We needed to make it less perfect. And as anyone in the industry knows, adjusting the brief halfway through a campaign means deadlines and costs often get blown out.
The final version we ended up with was a bit rougher around the edges, but it was necessary.
Another thing that became obvious through this process is how easy it’s become to mistake polish for purpose.
AI-generated content now rivals or exceeds human-created work across massive portions of the web. By late 2024, the balance had already tipped in many categories. That stat alone isn’t the problem – the problem is why.
Speed and frictionless production are replacing deliberation. Teams are shipping work that looks finished even if it took minutes to create instead of days. The question has now shifted from “Is this the right direction?” to “Is this ready to publish?”.
When creating something that looks finished, that takes minutes instead of days, we risk conflating output with outcome, volume with value and “good enough” with genuinely good.
Here’s where it gets tricky for our industry specifically.
Marketing has always been about standing out and saying something in a way that cuts through. That’s the craft.
But if the tools we’re using to generate ideas are all trained on the same corpus, optimised for similar outputs and rewarding safe, predictable thinking – how do we avoid becoming indistinguishable from each other?
The answer isn’t to abandon AI. That ship has sailed, and frankly, I think it’d be the wrong move anyway. The answer is to fundamentally change how we interact with what these systems give us.
Our experiment forced us to reckon with something uncomfortable: the first thing AI gives you is almost never the right thing to run with. It’s a spark but it’s not the answer.
Here’s what that means in practice:
Interrogate everything.
The moment something looks finished, that’s when you need to push harder. Ask what’s been smoothed away, what assumptions the model made and what directions got optimised out in favour of coherence. The rough edges are often where the truth lives;
Resist the path of least resistance.
Just because you can generate a hundred options in ten minutes doesn’t mean you should use the first one that’s good enough. Speed is valuable but only if it’s pointed in an interesting direction;
Make imperfection intentional.
We deliberately pulled our final version back from perfection because perfection wasn’t the goal – purpose was. Sometimes the most polished version is the least honest one.
The advertising industry has always been vulnerable to trends, templates and formulas. We’ve dealt with this before – when everything looked like an Apple ad, when every brand tried to sound like Dove, when “purpose-driven” became a checkbox instead of a commitment.
AI accelerates that tendency. It makes it easier to drift toward the middle. But when used with intent, it can help us generate unexpected combinations and surface connections we’d miss.
So yes, we used AI to recreate an LLM ad to criticise how LLMs create sameness. The irony isn’t lost on us. In fact, through play, it became the point. But, sometimes, to make people aware of the danger, you need to take them there. Because if we let convenience override craft, if we confuse ease with excellence, we won’t just end up with boring work – we’ll end up in a boring industry.
And none of us got into this business for that.
This article first appeared on Mumbrella, one of Australia’s leading media and marketing industry publications. Read the original piece by Pip here.
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