Golden age of ideas

In the light of the global reach and universal application of AI, and to paraphrase Winston Churchill as he was paying tribute to the Royal Air Force establishing air superiority over England in WWII, one might quip: "Never in the field of ideas was so much owed by so many to so few". The many, here, the users of GenAI, and the few the hard- and software pioneers and providers of AI models. Indeed GenAI is capable of producing instant ideas with a few prompts widening participation in the design process towards democratising design itself. And so, GenAI has become the ubiquitous ideation tool leaving users to "edit" or "curate" GenAI's results to their taste. That is, designers have become as much editors and curators of GenAI outputs as originators of their own ideas. But what is then an original idea? The French philosopher Baudrillard (1929-2007) argued that, in the age of multi media searching for the "true original" becomes pointless. Copy, then, becomes the new normal, and, one may add, even more so with the rise of social media and GenAI. Yet the format of digital copy and endless recycled imagery may, paradoxically increase the desire for seeing and experiencing the real thing as evidenced in young people's interest in analogue, tactile processes, such as film, or vintage items, such as vinyl records.

Designing in the age of AI

AI is widening participation in the design process making design more populist in both scope and manner: "Everyone is a designer". But research suggests AI tools are most benefitting designers who already are high performers. That is, designers who know how to guide AI, how to integrate it with everyday design tasks as well as how to judge its output. Yet extensive use of AI tools, notably by entry-level or early career designers may underplay first hand observation and drawing skills as well as critical thinking and judgement. Or, why struggle with a problem when a chatbot can provide a neat, packaged response that might be regarded as "sufficient," or "good enough"? What then is the impact of AI on cognitive effort in matters of design? Could it be the case that in accelerating AI technology, "Man shapes AI and then AI shapes man"? That is, rather than based on human agency, AI models develop their own agency. That is, AI agents can also make decisions and take action to achieve a goal without human prompted. What, then is design and what makes a designer in the age of AI? If, say, human agency is finite, then what it means to be a designer is to grasp this finitude of designing, or "becoming one who is designing".

Prompting is creative communication

The creative abilities of GenAI models are artificial, not genuinely human. However, with human assistance, GenAI can produce creative content. But, as shown in experiments, GenAI (DALL-E3) output, which are clear and measurable, depend on user prompts (written instructions) as much as models*. This highlights how prompting is about communication which in turn reflects how verbalisation (words) is the most common means of human communication. This in turn suggests prompting is a creative process as well as a skill - a skill that improve with practice. In short, GenAI models need human intervention to produce independent creative output. *https://mitsloan.mit.edu/ideas-made-to-matter/study-generative-ai-results-depend-user-prompts-much-models

 

AI-improv

Ideation has affinity with improvisation, or improv, a term derived from improvised performance and inspired by earlier theater forms such as commedia dell’arte and cabaret. At its core, improv is the art of free association, of making things up on the spot where improvised scenes often incorporate suggestions from the audience. But as an art form, improv favours the prepared mind. Similarly, ideators produce great ideas from their overall experience and observation*. And so, in the context of practising ideation, generative AI can be seen as an improv collaborator because interactive chatbots immediately respond to the user prompt by offering something new that directly builds on the user input. That is, working with improv dialogue, the chatbot responds to the user prompt with edits and multiple additions ("yes, ands"). But like any improv performance, the variety and quality of the output depends on interaction between the user and the bot. That is, the more the user brings curiosity, specificity and spontaneity to the improv dialogue, the more imaginative and meaningful the results become. Yet it should be noted that chatbots are not without bias, like people, but lack personal experience and human emotions. *There is no innovation without previous work. Also, "In the field of observation, chance favours only the prepared mind" (Louis Pasteur).

Creativity: Muses or neurons?

'Every act of creation is first an act of destruction', is a quote attributed to Picasso, words which suggest creating from physical matter, or creatio ex materia . This in contrast to the notion of creating "out of nothing", or creatio ex nihilo which proposes creation by some divine act or inspiration. Both assumptions involve the nature of creativity and change, and ego, And Picasso, like the Muses of Greek mythology, projected striking egotism, all that makes for a good story. But from a scientific perspective, creativity is elements of thought across many levels of physical, chemical, and biological description. Take Dr. Nancy Andreasen, for example, whose research is about the relationship between creativity and the brain. She writes that the brain’s abilities are “near miraculous,” and the process of creating something “is neither easy nor obvious.”. And so, her hypothesis is that the brain begins by disorganising and then making connections between various encoded data which were not previously connected. In other words, our brain is a self-organising system meaning that it could easily be chaos, with quadrillions of living, moving parts (neurons), but it somehow keeps itself in order, not unlike flocks of birds or ant colonies.  Now, if every human being possesses what could be called “ordinary creativity”, which goes back to our basic instincts, then our brains learn, unlearn and relearn to recognise patterns in order to aid in our survival. "Extraordinary creativity", on the other hand, as exemplified by Picasso, seems to be operating with substantially enhanced neural processes.  *Andreasen, N. (2005) The Creating Brain - The Neuroscience of Genius. Chicago University Press. 

Unpredictable AI

AI technologies are essentially computational systems and as such fairly well understood among IT professionals. However, from social and cultural perspectives, which necessarily include design processes and outputs, these technologies are poorly understood and, as there is no single definition of AI, have become subject to conjecture, speculation and doublespeak. But there are two primary approaches, probabilistic and deterministic AI with each serve unique functions based on their design and outputs: GenAI systems are probabilistic*. Yet AI's long-term impact on, say, education and the labour market is hard to predict because transformative technologies, such as generative AI, take time to become clear. Moreover, there is uncertainty about how humans, both individually and collectively will respond to AI's societal impact, particularly as the AI industry remains largely unregulated creating vast wealth and power inequalities. Given the unpredictable nature of AI, then, designers need to consider both technology and ethics when applying AI systems. *At a high level, probabilistic AI models uncertainty and provides outcomes based on likelihoods. This means that it doesn’t always offer one definitive answer but instead provides a range of possibilities with associated probabilities. Deterministic AI, on the other hand, is rule-based, designed to yield specific, predictable outcomes without room for variability once given a particular input. https://www.dpadvisors.ca/post/the-basics-of-probabilistic-vs-deterministic-ai-what-you-need-to-know

AI-deation workflow

Artificial intelligence-based software, such as ChatGPT has become an everyday part of designers' workflows, across disciplines and cultures. Using ChatGPT can help generate ideas and develop design concepts and make the ideation process faster and more efficient*. Moreover, AI image generators can save significant amounts of time and money on rendering. Indeed, as the underlying AI technology is getting more powerful exponentially, so are the tools based on the technology. However, ChatGPT responses and outcomes, and the quality of advice and solutions, depend on prompts, custom instructions and context, and while it gets a lot right, it doesn't get everything right. And so, powerful AI tools in the hands of beginners who don't understand what AI is doing are at risk of doing mistakes, and in haste. Therefore, it is good practice to verify anything important before acting on it, especially when it comes to high-stakes decisions. In short, to harness, and fully benefit from the power of AI across design fields require challenging experience and discernment. *Caveat: While complex ideas travel slowly, simple ideas actually travel very fast. However, there's a high amount of variables wrapped up in even the simplest of ideas, if and when we let an idea rest or “sleep on it.”

GenAI and the risk to critical thinking

GenAI, such as ChatGPT, has become immensely popular because the learning models respond to user prompts in a conversational way generating text, images, videos, and so on. That is, GenAI uses machine learning models (LLMs) to learn patterns from existing data, both structured and unstructured, to generate new and innovative content. But innovation needs a critical approach (1), not least in the digital age when transformative or impactful innovation is facilitated if not driven by AI. And so, designers need critical thinking skills in the process of finding a solution to a stated problem (2). But while GenAI is capable of creating new content, can it replace human critical thought and reflection? An online sample study of some 600 participants in the UK, showed that with people employing GenAI tools as substitutes, and not supplements to routine tasks, there is a 'significant negative correlation between the frequent use of AI tools and critical thinking abilities'. Similarly, a US university survey of 1,000 faculty found that GenAI diminishes students’ critical thinking skills and increase over-reliance on AI tools (3). Psychologists call it cognitive offloading when GenAI, offers users "good enough" outcomes that can be attained with minimal effort (4). But such outcome-oriented thinking that focuses on results rather than process may overlook or underestimate human judgement, user education level, design knowledge and experience.   (1) The term critical comes from the Greek word kritikos meaning “able to judge or discern”. (2) https://www.open.edu/openlearn/money-business/making-creativity-and-innovation-happen/content-section-5.2  (3) https://www.universityworldnews.com/post.php?story=20260128145305278  (4) https://www.psychologytoday.com/gb/blog/the-art-of-critical-thinking/202512/is-generative-ai-rewiring-our-brains-heres-how-it-happens

Superior human creativity?

GenAI models, such as ChatGPT use algorithms trained on vast amounts of data from existing work, and then generating their own novel content (texts, images, software code, and music). But are the models really creative in a human sense? Because the models don't "understand" the meaning or context of their creations directly, unlike humans who, generally speaking, show a greater ability to generate and evaluate ideas with great variations due to their unique and detailed training and experiences. For example, research suggests that generative AI–enabled stories are more similar to each other than stories by humans alone (1). That said, GenAI , as a tool, can support and augment human creativity acting as an "intelligent tutor" or "co-creator". So, what's the difference between GenAI and human creativity? Or, do people assume human creativity as being superior to GenAI? (2) Because if creativity is largely remixing parts and recombining thoughts, in which human language plays a major role, GenAI generates content in response to human-written prompts. That is, GenAI generates its outputs by statistically analysing the distribution of words or pixels or other elements in the data that it has been fed. And so, GenAI outcomes are based on likelihoods, hence probabilistic AI. But while GenAI in this manner "mimics" human creativity, it is not an original source of information and doesn't have the same kind of complex directionality as humans with their memories, intuitions, dreams and wishes. In short, GenAI is not an individual being (3). This highlights the ethical and social costs of GenAI harvesting human knowledge from often unidentified sources, that is, without consent or respect for privacy and copyright boundaries. That is, GenAI doesn't give credit to human creativity.  (1) https://pmc.ncbi.nlm.nih.gov/articles/PMC11244532/ (2) Traditionally, the study of the creative person focused on personality traits of creators with different level of achievement and in different domains.  (3) Cf. In the 1999 science fiction film "The Matrix," hacker Neo is faced with a decision. Resistance fighter Morpheus presents him with two pills. If Neo swallows the blue one, everything will remain the same — a comfortable life in a fantasy world. If he swallows the red pill, he will see "true reality" — a dystopian world in which humans are enslaved by machines. 

Choreographing ideas

The word choreography comes from Greek and most commonly refers to dance movement (in the sense, 'written notation of dancing'). Conceptually speaking, choreography is about symbolically designing movement in time and space. But what does it mean in practice? Can the notion of choreography be applied to design and, more specifically to ideation, as in idea sketching? While choreographers often “invent” new ways of moving, choreography transcends movements, and gives them meaning. Similarly, sketching is a performative process and practice that expresses and communicates ideas and give them meaning. And so, designers, like choreographers typically think about the various aspects of the task in hand and whether guided by improvisation or based on a brief. In this pursuit, designers and choreographers alike use a broad and varied range of means, from traditional techniques to experimenting with AI technologies. Regarding choreography as the art of designed movement, choreography is everywhere where movement happens. That is, choreography, in the digital age embraces places, spaces and settings thereby reinventing stage performance and so extending the audience experience. Indeed, theatrical experiences are a classic happening. And so, in its many forms and iterations, choreography - and sketching - can be conceived both as dialectical practice and performance that, though disruptive in character, is constructive in outcome gesturing towards a state of being-without-limit.  https://www.dance-masterclass.com/blog/what-does-a-choreographer-do

 

Dialogic ideation

One of the many approaches to design ideation takes the form of dialogue, or interactive communication between two or more designers, or between the designers, clients and users (co-design). The dialogue, or conversation, however, as an experience of human interaction, is rarely just a rational exchange of ideas. More often than not it involves the participants' attitudes, motivations and feelings when faced with, and responding to a given situation or problem as typically articulated in the design brief.. The dialogue, or exchange of first thoughts and ideas, then, creates and presents scenarios or narratives that triggers emotional responses in the stakeholders. Moreover, in AI-facilitated design, the dialogue highlights the role of designers to take responsibility and accept accountability for limiting any negative impact of AI, notably in terms of AI ethics. Additionally, the emotional content in what may called dialogic ideation, may act as a prologue to designing for emotion when, as in, say, product design, the user experience is core. 

When ideas run into the sand

When Saudi Arabian Crown Prince Mohammad bin Salman proposed building a 110-mile linear megacity called the Line in the middle of the desert near the border of Jordan and Egypt in 2020—the centerpiece of a vast new planned city called Neom accommodating 9 million residents—a dozen of the world’s most prestigious architecture firms signed up. Now, five years later, the idea has run into the sand, literally, with the Saudi government having a change of heart announcing the $1.5 trillion project will be downscaled drastically. And so, the assigned architects are now working on redesigning the megastructure into a more modest, and radically changed project - from metropolis to a hub for data centers to serve the AI industry. The megacity idea, however, was, due to is complexity and construction costs, unrealistic from the start. More generally, expert analysis by Oxford University's Saïd Business School suggests megaprojects are often commissioned for the wrong reason ("unchecked  motivations") and, due to their size and complexity, are pre-determined to systemic failure. As for motivation, then, did big ego, including the architects', get in the way ("lack of ego-control")? If so, does the retreat of the megaproject exemplify design driven by hype rather than evidence-based practice. For example, Neom's presentation videos were a masterclass in rendering a seductive utopia. More contentious, the Saudi decision may reflect how the planning of Arab cities responds to globalism: That is, does "new Arab urbanism" differ significantly from colonialism, that is, the intrusion of a Western lifestyle? http://ndl.ethernet.edu.et/bitstream/123456789/20950/1/11.pdf

The idea of progress

Design ideas are intrinsically linked to the Western idea of progress and the 4 industrial revolutions, from the end of the 18th century to present-day (mechanical revolution, mass production, digital automation and industry 4.0). That is, in evolutionary terms, progress built on the assumption of gradual, continuous change - a self-driven, purposeful and cumulative process. However, since late twentieth-century, the idea of progress has increasingly come under scrutiny as a result of the environmental and social consequences of free-market policies. And so, with industry 4.0, the question is now how AI systems will impact the idea of progress in the perspective of economic and social development, particularly when the effects of AI remain uneven among jobs and economies as exemplified by the introduction of humanoid AI robots. Positively, AI may renew and reaffirm the idea of progress as experimental technology turns into operational technology in homes, offices and on factory floors  However, the uncertainty or disruption triggered by AI - revolution or evolution (?), may erode the historical foundations for the belief in progress as a human force for good. To consider future direction and impact of AI on society at large, understanding how it is used, and why is as important as measuring how widely it is adopted.

Ideas looking for a problem

From loudmouthed AI chatbots to ridiculous e-commerce product summaries, “AI slop”, which is defined as low-quality and generally unwanted AI-generated content, reached a new peak in 2025. Indeed AI-generated articles now make up more than half of all English-language content on the web, according to search engine optimisation firm Graphite. And so, in AI slop environments, designers find  themselves under commercial pressure 'to start from the solution and work backwards to find the problem', say researchers at Nielsen Norman Group. That is, product designers have been tasked with integrating AI almost anywhere and everywhere even when it might make little sense. And so, there has been a reaction against the amount of AI slop spreading on the web although, as the research suggests the tide seems to be turning against AI slop making way for more intentional product design and strategy that focuses on impact. If so, more intentional product design, given he impact of AI, suggests that designers will need to argue their case for what their skills contribute to projects. Interestingly, working backwards from problem to solution has a parallel in the found object art, that is, a non-art item or ordinary manufactured objects is designated as art, as exemplified by Marcel Duchamp's "readymades". André Breton, the co-founder of surrealism, defined a readymade as a ‘manufactured object raised to the dignity of works of art through the choice of the artist'. And so, Duchamp put his idea about the object and then calling it art.  Readymades are considered a viable artist practice although still open to questioning.

Remote ideation

Major changes have taken place in design practices in recent decades strongly impacted by technology, including digital communication. Positively, this means that designers can work from almost anywhere they choose. But freedom to work almost anywhere and at anytime may have drawbacks. For example, social isolation and loneliness may ensue with technology-based remote working as many aspects of design relate to the physical world with its many sensuous qualities, including in-person interaction. Indeed, as held by the philosopher Merleau-Ponty (1908-61), actual human existence is perceived on a sensual-physical level*. Physical studio space, however, is no longer the default setting for designers as AI systems combined with CAD and 3D printing provide for mobility and remote working, from ideation to prototyping. That is, AI assisted design thinking and making are changing the studio experience. Yet most designers feel the need for physical and ambient work spaces not just for work-related discussion and collaboration but for spontaneous meetings and small-talk that provide both social and emotional connections. After all, digital communication, however convenient and efficient is lacking built-in social breaks. And so, to stay motivated and productive, designers want social face-to-face connections. Studio observations, moreover, and both in education and professional practice show that person-to-person interactions foster and enhance creativity and collaborative work as well as giving designers a sense of belonging to a team or community. Text inspired by https://www.psychologytoday.com/us/blog/digital-world-real-world/202512/remote-working-and-loneliness?utm_source=firefox-newtab-en-gb  *Merleau-Ponty, M. (1945 French original - 1962 English translation). Phenomenology of Perception. London: Routledge & Kegan Paul

Learning from Frank Gehry

Buildings designed by the architect Frank Gehry (1929-2025), such as the Guggenheim Museum Bilbao (1997), have gained wide public admiration as well as inspiring fellow designers. But equally noteworthy is his design philosophy: 'To design something that one would want to be a part of, something one would want to visit and enjoy in an attempt to improve one’s quality of life.' More specifically, Gehry considered architecture 'to be art' aiming at transferring the feelings of humanity through inert materials. Not surprisingly then, he greatly appreciated the visual arts, notably sculpture which influenced his architectural approach resulting in innovative and unconventional forms. In this, he experimented with industrial materials and methods, such as overlapping glass panels and titanium cladding. But key to Gehry's creativity was the role of preliminary dynamic sketches in generating design ideas (observing here too, for example, Paul Klee’s serpentine lines as the essence of creative thought). That is, he began the creative process with freeform sketching and modelling visualising what he had in mind and then turned the ideas into production-ready drawings and material form with the use of scanning processes and 3D modelling software. Gehry also took a keen interest in education encouraging students to always be curious, and let architecture open up to other subjects, such as philosophy, literature, and music. Furthermore, and although he resisted categorisation as an architect, despite recognisable deconstructivist architectural elements in his work, he advised students to study and learn from the greats, from Brunelleschi and Borromini to Le Corbusier and Zaha Hadid.

AIdeation: ChatGPT three years on

ChatGPT, the generative artificial intelligence chatbot, was launched three years ago (November 2022) and has evolved rapidly to become the go-to software tool for generating, developing and communicating ideas, enhancing concepts and exploring scenarios. But more than this, ChatGPT, together with CAD and 3D printing is increasingly facilitating and streamlining the design process - from first thoughts to prototyping. Indeed, rare is the designer who hasn't engaged with ChatGPT, which holds 61 percent of the market for Generative AI chatbots. But despite its widespread adoption, ChatGPT is not without risks. For example, it can pose a threat to integrity, such as plagiarism (although copyright does not protect ideas or concepts per se), or create technology dependency. But while the impact and implications of ChatGPT are felt and experienced across design fields, both in education and practice, the chatbot has become a powerful assistive design tool offering creative synergy between human ingenuity and AI. Yet like any computer tool, ChatGPT should be used from critical and informed perspectives, considering both its benefits and limitations as a creative source for best course of action or possible outcome. After all, ChatGPT is only as good as the user's prompts. This highlights the role of judgement in creative thinking which may suggest building foundation for critical thinking without relying too much on GenAI systems, especially for novice designers. That is, to encourage critical thinking, in the context of design education and creative learning, to develop fortuitously through personal engagement with tools and materials that offer tactile sensations and emotional connections. That is, to empower users of ChatGPT, in often cluttered and noisy digital environments to (re)discover arts and craft tools for inventiveness. But overall, the challenges presented by chatbots call for ongoing experimentation, research and discussion.

Ideation, GenAI and critical thinking

In an analogue world, design ideas aren't forced upon the designer. They are conjectural, or guesses about reality - a proposal or tentative solution to a posed problem. In this pursuit, designers aren't idealists because design ideation is a purposeful activity aiming at realisable ideas. The designer, then, is seen as a realist seeking to be proven right. That is, the designer faces practicality having to accept the physical facts of the situation and, oftentimes the emotional side to the problem at hand. Yet some ideas can clash with reality, and when they do they remind designers that ideas may be mistaken. Ideation, then, is a process that must allow criticism in order for the idea to move forward, to propose a better solution. That is, ideation includes critical thinking skills. And so, designers not only need to be imaginative and open-minded but willing to be corrected. However, with the rise of Generative AI, what is its impact on critical thinking? Interestingly, a recent survey (2025) shows that in GenAI-assisted tasks higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking.* This may suggest that practsing ideation without GenAI assistance could help foster greater critical thinking skills also raising designer self-confidence in problem-solving ability. https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/

Ideation and problem-solving

When designers respond to a problem, the problem is typically on a sliding scale from simple to complex.  Roughly speaking, simple problems can be described as puzzles, or "tame problems", whereas complex problems are known as "wicked problems". A puzzle is fairly straightforward when the pieces need to be located and connected. That is, it is assumed that the solution to the puzzle is almost certain to be found. A complex problem, in contrast, has no known or at least immediate solution and so designers are faced with challenges that call for a greater variety of skills and capabilities involving collaborative and decision-making tools and techniques. But whether simple or complex, the starting point for problem solving includes clarifying the goal, identifying the constraints, and understanding the context of the situation. Also, see Tame vs Wicked problems, in blog below. https://www.td.org/content/atd-blog/puzzle-problem-challenge-or-conundrum

Conversational prompting

Prompt engineering is the process of writing effective instructions for a GPT model, such that it generates content that meets desired outcomes. And because the content generated from a model is non-deterministic, that is, even for the same input, the model can generate different responses on different runs - and the rich datasets combine art and science, prompting can be particularly useful in creative fields such as design. However, there’s more to effective human-AI collaboration than a perfect prompt. And so, conversational prompting is a technique that involves interacting with AI systems like ChatGPT in a human-like conversation. That is, users describe in everyday terms what they want ChatGPT to do rather than trying to craft complex prompts. In this, users engage in back-and-forth interactions with the model to refine the results, provide additional context, and answer the ChatGPT's questions. That is, the user guides the process while letting ChatGPT handle the specifics of generating appropriate prompts and responses. Problem solving, then, it is thought, is enhanced by balancing human ingenuity and machine intelligence. However, conversational prompting, as a feed-back model, carries risks too. That is, the interaction may contain misinformation, biases or illusions raising the question: Is the output reliable and trustworthy? Selected sources:  https://platform.openai.com/docs/guides/prompt-engineering    https://promptengineering.org/conversational-prompting-in-generative-ai/