The era of art scarcity is almost over. As productive AI makes producing high-quality creative goods much cheaper and faster, we are entering a new paradigm of “infinite creativity”. This solves a common productivity bottleneck but creates a new, more complex challenge: in a world where we can do anything, how do we decide what to do?
This article explores how to navigate this new reality by envisioning a “creative fitness zone,” where the peaks represent the best performing arts. Since real-world data for testing is limited, we have to use clever techniques to find these peaks. We propose a two-part toolkit:
The “Exploit” Strategy: Using AI to make a small difference in successful innovation for local good.
Strategy “Check”.: Making data-informed “big leaps” to find new creative areas of success.
Looking ahead, we suggest that a major key to unlocking this landscape is removing the data barrier itself, perhaps using artificial audiences. However, this future is uncertain, as the accuracy of such instruments has yet to be determined.
The end of the product as a creative bottle
Sales are evident in several ways from the data in Graph 1 – reduced profits for those who started early, e.g. The stability of the orange AI, but also the integration of model points – everyone’s models are placed in the same way. It is worth noting that the prices of photo and video production are stable at the moment but the trend of the same technology of LLM has decreased rapidly and its production of photo and video is expected to do the same.
Creative production is an obstacle that affects not only the speed of launching a campaign but also its overall performance. We know that creativity drives 70% of campaign performance1 and that creative diversity is the key to driving performance2. Therefore, a slow, resource-constrained creative process has a significant and direct impact on marketing effectiveness.
However, the future promises to be very different. The quality of generating images and video AI tools has grown rapidly and at the same time the market seems to be targeted for sales.
So what is the result of creative production of high quality and always cheap? In the next few years, the ability to create any art we want, with any content we want, will become a reality. In simple words, we will have an endless collection of creations to choose from.
Endless creation. Don’t let that sink in
A fitness landscape for endless creativity
Let’s imagine that every creative possibility can be represented in a 2D grid like the one below in Figure A. Each point in the grid has a unique creativity, with little compositional difference between adjacent points.
We know that some of these creative opportunities will do better than others. So we can add a 3rd dimension to the grid; the vertical axis here is fitness, which we will loosely define here as creative performance. The “peaks” are the ads that perform best, and the “valleys” are the ones that don’t play.
We determine the suitability of each art through live testing, but testing every option in an infinite space is impossible. Imagine running a million creative variants on an account. The daily clicks or conversions will be so low as to be statistically meaningless. Herein lies a new contradiction: while creativity is limitless, the data needed to test it is not
All this means is that we start with figure A but we cannot shape figure B on top.
Our basic issue is now clear – we can make any image in this area now thanks to the AI that produces it which is a great release from the traditional creative barrier, but looking at our newly discovered data limit feature, how can we find out which creative variations we should examine closely to increase our chances of getting improvements compared to control?
Put another way: How can we intelligently navigate the creative space of fitness to maximize artistic performance?
The Navigator Toolkit: Use and Test
To answer our navigation challenge, we have two complementary strategies:
- Use our current position to find the highest price for the property
- Explore further to escape local peaks and seek even higher ones
Generative AI makes both techniques faster, easier, and more efficient than ever before.
The “Exploit” Strategy
Early testing at Brainlabs:
We’ve been playing with small scale variations throughout the text and art, allowing us to increase the diversity of the art on the platforms, but also gradually move towards local excellence. An example of the creative results below:
The methodology can be applied directly to advertisements, keeping all other features the same.
Goal: Carefully mapping out the best known landscape to find its highest elevation.
Method: Use generative AI to create a number of microvariations that create the highest possible performance, this is found in the same area of the world’s fitness. Continue to choose the most effective art and make many variations using that as a seed.
Why it works: This method has two advantages. First, providing more creative diversity gives bidding algorithms more options, allowing them to better match the right creative to the right user and context. Second, by continuing to use a top player as the ‘seed’ for the next generation of variants, you create a fast, data-driven cycle of gradual improvement.
“Check” strategy.
Relying only on the exploitation strategy can keep you stuck at the ‘top of the range.’ If you ever expand your current position, you will never risk a temporary dip to cross a valley and find a higher mountain. Therefore, we need an experimental strategy: a way to jump into a new landscape with higher chances of success.
Goal: To escape “local peaks” and find a completely new environment, which may have higher performance.
Method: Take historical creators with high performance, label them to understand the content and produce new art based on this label, or a variety of different art without reducing them to their labeled parts.
Why it works: This strategy uses historical performance data to inform its escalation. By analyzing the characteristics of past winners, we can identify other creative landscapes that may be fertile ground. This makes exploration less of a shot in the dark and more of a calculated, strategic leap. Example: Imagine an insurance company’s AI analyzing the best-performing trailers in the film industry, identifying ‘fast editing’ and ’emotional impact’ as key attributes, and then using those criteria to generate a new, dynamic ad idea.
So we’ve discussed how “Exploit” and “Exploration” strategies are powerful ways to explore the creative space and discover new player creativity, all powered by AI. However, although powerful, these techniques are ultimately limited by the speed and cost of obtaining real-world performance data.
What if we could release the main barrier we have put up? What if data is no longer the limiting factor?
Moving forward into the future – removing the data limitation in innovation testing
In order to truly explore the infinite space, we must remove the data limitation mentioned above – a task that can only be done by artificial observers.
A synthetic audience is a complex simulation of a person, or possibly a target market, built from a combination of LLM results and real-world data. They aim to accurately represent the audience with a measurement that allows them to be used as a focus group
Using these audiences to generate a set of synthetic performance data may allow large-scale pre-testing across the creative landscape. If we assume that the computational cost is not zero, we would not be able to test everywhere but we can go much faster if we use an exploit method and remove our test jumps from risk. It is also possible if we allow the creativity to lose for a long time that we may not need to jump at all and we can move slowly between peaks and valleys just like an exploitation strategy.
If the speed of the model increases and the costs decrease in this technology – as it should be given that it is based on the output of the LLM, as discussed, which may be an asset – we can even think of a world where this very effective creative research can be carried out in the auction area, using real-time signals to reduce the area to be tested and then the techniques of evaluation and exploitation to sharpen about that person – if we think about everything except the DC that we know everything about. speculation.
Caveat – and it’s big. This future hinges entirely on the idea that artificial audiences in the future can accurately predict the real-world effects of artificial objects placed in front of real people. The jury is still very much out on that.
Conclusion:
We stand at the starting point of a new creative era. The production bottleneck that has defined marketing for decades is ending, replaced by an endless field of creative possibilities powered by generative AI. While this presents an incredible opportunity, it shifts the core challenge from production to selection.
As we have explored, navigating this world requires a two-pronged approach: leveraging known success in surgical precision with minimal variation, while simultaneously exploring new areas with data-driven creative leaps. The future promises an even greater acceleration of this process, where artificial audiences can remove the data limit entirely, allowing for creative experimentation on a previously unimaginable scale.
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