On the day that Richard Feynman died, he had a poignant message scrawled across his blackboard: “What I cannot create, I do not understand.” As an expert in theoretical physics, he was referring to the idea that in order to truly comprehend a concept, or be effective with it, you must first be able to create that concept from scratch. While this may still hold in some areas of science and math, with everything that is happening with generative AI, I wonder if this remains true for creative work?
As creatives, we are now at a crossroads. While AI tools offer new possibilities, they also create a host of new challenges and requirements meaning that as creative work gets easier to execute, it also becomes infinitely more complex to apply. Which begs the question: now that we can prompt our way to seemingly passable creative, does that mean we still understand it, or can be effective with it?
On one hand, AI-assisted tools are simplifying and accelerating many aspects of the creative process. Figma’s ‘Make Design’ for instance, allows designers to prompt designs rapidly. While tools like Wireframer, Spline, and Creatie.AI streamline wireframing, 3D modeling, and component creation. Artificial intelligence is even transforming user research, reducing our interview analysis time from days to less than an hour, using automated AI transcription and data analysis. Across the industry, generative AI is dramatically improving the speed and quality of digital work output.
However, there seems to be a growing sentiment that because we can now achieve “good enough”, with less effort, we should settle for that. We should think small, short term, extract as much value as we can with minimum effort and hope for the best – without pausing to assess if that quality of output is good enough to distinguish us from anyone else with access to the same tools.
AI’s propensity for optimisation risks standardising creative output into a “sea of sameness.” We’ve already seen the impact of AI-generated content, estimated to comprise over 90 per cent of the internet, with posts sounding increasingly similar due to overreliance on tools like ChatGPT without proper prompting or custom model training. To counter this, creatives need to employ new workflows and essentially force the creativity from these models using techniques like Collaborative Chain of Thought Reasoning.
To add this, the pressure to deliver faster in an always on world, means creatives must constantly learn new AI tools and skills, which are evolving more rapidly than any individual can keep pace with. At Create group, we manage this through a curriculum of trainings, workshops, hackathons, and dedicated experimentation time. We do this across department because arguably, no matter the role, keeping up with generative AI will soon become a significant part of everyone’s job.
Moreover, the rise of artificial intelligence is reshaping user expectations and introducing novel design challenges. AI assistants are becoming more context-aware and interactive and designing for them or with them demands careful consideration to avoid pitfalls like Google’s recent AI blunder advising the use of superglue on pizza. Meaning designers must design for entirely new AI-enabled experiences for both human and AI users, while also optimising for AI search engines and aggregators with frequently shifting algorithms.
These changes can be daunting, especially when it’s difficult to keep up. The exhilaration of wielding powerful new AI tools can quickly give way to a sense of diminished importance as even more advanced capabilities emerge. But rather than resisting the inevitable, creatives should aim to harness their curiosity to learn, adapt, and explore the new possibilities AI affords. Executives, meanwhile, should double down on upskilling their teams to generate novel value and outperform competitors fixated solely on cost-cutting.
Nonetheless, the AI revolution has made design exciting and unpredictable once again, enabling the creation of previously unimaginable brand experiences. Meanwhile user experiences are poised to become more natural, relevant, and engaging. Capitalising on this potential, however, demands comfort with adaptability, keen taste, intuition and knowing how to control AI output.
So as AI-generated creative output proliferates, perhaps Feynman’s maxim needs an update: “What I cannot control, I do not understand.”
By Romain Colomer, Experience Director, Create
