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Ireland faces a critical housing shortage. Our financial system means a short-term reduction in house prices is unlikely [1], and analysis points to a shortage in supply as a key contributing factor. To build ourselves out of crisis, housing will need to be delivered at large scales. State agencies, housing bodies, and developers are masterplanning significant swathes of land. In the face of a climate crisis, hitting our housing targets is just the first step. A major challenge will be how we can sustain our housing supply for generations to come.
When we consider our building strategies through the lens of repair, a key factor that emerges is scale. Repair has a certain set scale because buildings are not precise. The rolling list of repairs that a building demands does not lend itself to a top-down, large-scale approach, but instead requires careful, human attention. Learning to build is a lesson in measuring twice, cutting once, and allowing for errors. This is not precision engineering; small misreads can become gaps you can put your hand in. Our houses are full of filler; half of Ireland is held together with Tec7. If we more carefully consider this complex and often unkempt side of construction, we can enable the continual transformation of buildings over time.
Large-scale, new-build construction might seem re-assuring for hitting housing delivery targets at first. At the macro level, however, it remains challenging to strategise for ongoing repair work: problem-solving with limited clues, incorporating room for error, and facilitating creative on-site responses. The onus for delegating and procuring repair in private housing typically falls to management teams, resident committees, and individuals. Not surprisingly, meaningful change is impeded by financial realities. We are fast approaching a crisis point; a 2018 report by the Royal Society of Chartered Surveyors of Ireland found three-quarters of apartment complexes do not have enough funds set aside for long-term upkeep [2]. Post-occupancy evaluations are unfortunately neither mandatory nor regularised. There is a glaring absence of data on maintenance and repair in our climate and economy.
If we reconsider housing with repair in mind, then we begin to look beyond the hoarding of new-build construction sites. We have a country filled with crumbling brick terraces, mistreated stone townhouses, and swathes of cold, damp bungalows tucked into the corners of fields. While this dimension of construction might not be appealing to large-scale investors, for owner-occupiers, repair and restoration could present an invaluable opportunity to obtain and customise a place of one's own. The state is attempting to encourage these small-scale repairs by offering grants of up to €70,000 to homeowners undertaking renovations of derelict properties. But this money is only released once the project meets a number of strict conditions, and is only available to projects involving a contractor [3].
The potential for this mode of construction is significant. A high proportion of an entire generation are currently locked out of the property market. Their powerlessness to enact change is contrasted with our historical culture of self-building in Ireland. Although a frequent topic of derision, the Bungalow Bliss building phenomenon in Ireland was a remarkable feat of small-scale building, happening en masse. The effect of this goes beyond housing quanta, as writer Adrian Duncan notes: "There's more of a direct relationship with a home you build for yourself when compared to moving to a house built by a stranger". He describes the Bungalow Bliss period as "one of the last few unselfconscious instances in Ireland of the traditional meitheal" [4]. This could equally apply to a house one maintains, repairs, extends or retrofits.
However, current government supports fail to reach self-builders and DIYers. Instead the system relies on the appointment of contractors. David Byrne, a self-builder I spoke to, lamented the lack of support available to him: "The government, while meaning well, is giving money to the middle man, while the person at the bottom isn't getting any benefit" [5]. His current project fits many of the criteria that the government seeks to encourage, but as he is aiming to do the work himself, the need to have a contractor involved removes any chance of receiving support. With the uncertainty of older buildings, the need to pay for work upfront, and inability to carry out the work by oneself, many of the grants are a helping hand that is simply out of reach.
There is also a lack of nuance in these government supports that is stifling the building economy. When it comes to thermal improvement, many grants rely on using an improved BER rating as evidence of a job well done. But the reality of retrofitting is far more complex. In David Byrne's case, he is removing the non-breathable elements from the existing stone walls, to restore their innate thermal and hydraulic functions. Yet he struggles to find support for this approach: "Ninety percent of people I talk to in the building industry tell me to dry-line the stone walls" [6]. How can we expect to harness the energy and desire of self-builders, if we cannot offer them more flexibility in financing and delivering their projects?
Enabling DIYers will require a surge in specialised education and training, funding and political vision. Currently, a significant barrier to a widespread DIY approach to housing is the availability of clear and accessible information. Some efforts are being made; enterprises like Common Knowledge aim to teach lay people construction skills with in-person courses. Similarly, architects and engineers will have a role to play in encouraging this shift in building culture. Already advocates for self-building – Walter Segal internationally, and Dominic Stevens domestically – have proven the potential for architects to enable self-building. The effort could be worth it, a widespread self-build and DIY revolution has the potential to tackle a number of the problems faced by the current Irish housing landscape: supply, lack of tradespeople, and vacancy.
If we can navigate this successfully, we could create a new self-perpetuating system for housing delivery and maintenance. If we want the job done right, we'll have to do it ourselves.
Future Reference is supported by the Arts Council through the Arts Grant Funding Award 2024.
1. J. Matthews, 'Taoiseach: Banks would 'think twice' about new mortgages if Dublin house prices lowered to €300k', TheJournal.ie, 2023, Available at: https://www.thejournal.ie/taoiseach-mary-lou-mcdonald-house-prices-dublin-6255715-Dec2023/, (accessed 28 February 2024).
2. B. Murphy, 'Majority of apartment complexes don t have funds for long-term upkeep', Dublin Live, 2018, Available at: https://www.dublinlive.ie/news/dublin-news/majority-apartment-complexes-dont-funds-15290101, (accessed 12 February 2024).
3. Vacant Property Refurbishment Grant, Citizens Information. Available at: https://www.citizensinformation.ie/en/housing/housing-grants-and-schemes/local-authority-housing-grants-and-supports/vacant-property-refurbishment-grant/, (accessed 16 February 2024).
4. A. Duncan, Little Republics: The Story of Bungalow Bliss, Dublin, The Lilliput Press, 2022, p. 146.
5. D. Byrne, interviewed by author, 2024.
6. D. Byrne, interviewed by author, 2024.
On the Research & Delivery Team at the GDI Hub, Rebecca delivers applied research to inform more inclusive policies and systems through participatory, user-centred approaches, often with partners in low- and middle-income countries. What follows, after an introduction by the interviewer, is their conversation, lightly edited. Rebecca speaks in a personal capacity; the views expressed are her own and do not necessarily represent those of GDI Hub.
This interview explores research through the lens of disability inclusion research, which is contending with the same questions broader architectural research is: how will AI impact the co-design, to thematic analysis, to the transcripts and workshops that research like this depends on. Building on these shared concerns, this field deliberately focuses on inclusion, a topic frequently underexamined in architectural research.
These issues matter, because our research often shapes the policies and standards our buildings are held to. And we are already slow to design past the minimum of Part M, with research finding only a small minority of new housing in Ireland even functionally accessible [1]. With AI, we can now produce work faster than ever with tools whose biases we may not have critically examined. Their images and renders quietly omit persons with disabilities, allowing exclusion to be embedded in what we make and the possibilities we present to clients, without us even realising. On the flip side, all is not lost, as, when used well, the same tools could open up opportunities for people previously shut out of design, as both clients and as architects. Setting my views aside, I unpick this with Rebecca in conversation below.
AM: Before we get to AI, could you briefly describe your role, and the barriers you're most concerned with in disability inclusion, especially where they meet the built environment?
RJ: I'm a researcher in disability inclusion and assistive technology at the Global Disability Innovation Hub, working on international research and consultancy with a wide range of partners (development banks, governments, disability-led organisations) around the world, with a lot of work in low- and middle-income countries. Much of our research focuses on assistive technology, from wheelchairs and prosthetics to hearing aids and screen readers, through our flagship UK International Development funded AT2030 programme, which tests what works to improve access. The social and human-rights models of disability show how environments, attitudes and policies can be just as disabling as someone's impairment. The built environment is central to that: if a wheelchair user is trying to enter a building that has only stairs and no ramp, they can't yield the benefit of the space, whether that building is their school or their workplace. So we're always looking at the why behind a technology: enabling people with disabilities to live the lives they want and thrive in their communities. On AI, the challenge right now is balance. As a London-based organisation working in partnership with disability-led organisations across the Global South, there are both the opportunities and risks, as well as power dynamics to be mindful of: AI-generated inputs that aren't someone's own words can dilute their lived experience, but AI can also lower barriers, like plain language or translation, for partners and researchers with disabilities. AI should never replace that lived-experience input; at the same time, as these tools spread, I believe we have a responsibility to help level the playing field, including providing guidance on safe, responsible use.
AM: There's a risk in inclusive work of flattening real difference into an 'average' user who doesn't exist. When people come to you through grant applications or co-design, do you think AI advantages those who have it and homogenises everyone else?
RJ: I'd agree. There are opportunities and risks on both ends, and I've definitely seen homogenisation. For research with grassroots organisations, promoting that authentic voice in their own words is really important; when survey responses come back as AI text, there's a huge risk of missing those authentic experiences. AI-generated text should not replace participants voices; outputs have to reflect the thinking, expertise and values of the people involved. So we need to be very clear: for one project, I said we'd rather have voice recordings or bullet notes. We're not looking for polished prose; we really want it to be your voice. At the same time, in a system where professionalisation standards sit within a Western, NGO-ized framework, organisations of people with disabilities in low- and middle-income countries can be excluded from funding for not writing in a certain way, or not having a technical grant-writer. AI can genuinely lower those barriers too. You have to hold both.
AM: Architects and designers lean heavily on renders and images, and researchers likewise rely on images to communicate their findings. As AI image tools often leave disability out, how do we keep our dissemination representing persons with disabilities authentically?
RJ: The AI-generated images are still something I struggle with, honestly. I think it's really important to keep supporting illustrators and graphic designers, including illustrators with disabilities, and to represent that diversity in our work. There's potential for AI to visualise information in new ways that make it more accessible, but surface-level visual appeal should never stand in for depth or authenticity. From my experience, when AI images are used, in a workshop say, it s always declared, and it's usually just a starting point. Sometimes it is to show what something like a journey map might look like; sometimes it is a mock-up a researcher without illustration skills can use to convey an idea, which an actual designer then takes on later. I haven't found it consistent or impressive yet, and that's partly how it works, going off probabilities. We need clear frameworks to uphold responsible, intentional and ethical use.
AM: As a qualitative researcher, where do you draw the line between AI as a help and AI doing the work?
RJ: AI models can act as a mirror of society. There's that term WEIRD: Western, Educated, Industrialised, Rich, Democratic. And we're still a very medical-model society, viewing disability as something to be fixed, with people with disabilities often excluded from the datasets these models are trained on. Those same biases get baked in, so you have to check for them. With thematic analysis, AI tools can help summarise or paraphrase to support initial coding, but that should still be human-led, with human review. There s also ongoing debate about whether AI should be used for this at all. Braun and Clarke, who are the go-to authors on reflexive thematic analysis, even put out a paper arguing against using generative AI. I think of a colleague, a public-health researcher and data scientist, whose expertise on a project in India was essential to validating how we framed our statistical claims. That kind of judgement is exactly what I don't think AI can replace. I believe we should use AI in ways that enhance our value, strengthened through critically interrogation and human expertise. And wherever AI meaningfully contributes, it should be documented and disclosed, for transparency and research integrity. One more thing worth flagging is data protection. Using these tools often means handling personal and special-category data, the sensitive kind, including health and disability information. Where that data goes, and how it is stored and used, needs to be a serious consideration every time, and even more so in work with disabled communities.
AM: When AI is used to carry out a literature review, do you think there's a loss in the knowledge a researcher would otherwise gain from doing it themselves?
RJ: I've used tools like NotebookLM and Research Rabbit to organise sources and draw connections, but if you let the AI choose your sources, its search strategy has its own biases, and there's real knowledge lost. If you're not actually reading the literature, you're not gaining what comes from doing the review.
AM: On the more hopeful side, could AI widen who gets to take part, and who gets to be a researcher?
RJ: There's a lot of potential there, for example, with accessible science communication and access to knowledge. We have Founders Think at GDI Hub, built on our accelerator knowledge, so a designer or entrepreneur with a disability who faces barriers to entry to an accelerator could work with it on their own time. People with disabilities often face reduced social participation and smaller networks, and digital barriers can shut them out further, even from applying, if a website isn't accessible. Not gatekeeping that knowledge is a real opportunity. It also has applications in inclusive education, like simplifying language to different reading levels, which can help someone with low literacy or a learning disability. So there's genuine potential for accessibility, alongside the downsides around biased data.
AM: This is something I feel strongly about. If the people trying to do good step away from AI, we leave it to be shaped by everyone else. Do you think we have to engage with it rather than opt out?
RJ: So many people are trying to use it for harm that we have to be in there too; it learns from us. I think about the Centre for Digital Language Inclusion, a GDI Hub project using AI to support people with non-standard speech in African languages, a brilliant example of AI for good. But at the same time, more broadly, there's the risk that generic AI transcription tools don't capture those same people. It's often two sides of the coin. As a paper by Musslick and colleagues put it, the use of AI in science is not a neutral act: it reshapes workflows, influences which questions get asked and affects how evidence is weighed and interpreted .[2] So in my view, we need to upskill rather than turn away, keeping the expertise that lets us stay nuanced and staying transparent about how we use it.
AM: If we spoke again in ten years, what would tell you AI had been a positive influence on your field?
RJ: I'd love to see it democratising access for people with disabilities to become researchers and reach scientific knowledge, reducing those barriers to entry. That would be a really amazing outcome. And I'd want a clear framework on best practice for AI in disability research. The technology has moved too fast for us to catch up; without a shared framework it's hard to know what, when and how to apply it ethically. There are general principles for responsible AI, but there's a uniqueness to who we work with: people with disabilities in low- and middle-income countries at the very edge of exclusion. So we need something specific to that. Right now it seems like a bit of an impossible dream, but I'll put it out there anyway.
What stayed with me from this conversation was the importance of engaging with AI rather than stepping away from it. Rebecca and I share concerns about the biases these tools can reinforce, the voices they risk excluding and the knowledge we lose when we allow them to do our thinking for us. Yet we also recognise their potential to open doors for people historically excluded from research and design. Rebecca also reminded me of the low- and middle-income contexts I have previously worked in, and where she continues to work, where people with disabilities already face disproportionate barriers to everyday life, including access to basic needs such as clean water and reliable energy. In this context, the environmental cost of AI is not simply a concern for climate specialists, such as Erida who I spoke to last month, but a question of equality and who bears the consequences of technological progress. Perhaps the challenge is not simply how we use AI to make our work more inclusive, but how we ensure that, in doing so, we don't create new forms of exclusion.
AI declaration
This interview was conducted in conversation and transcribed with AI-assisted transcription using Microsoft Teams; the transcript was then edited by the author for length and clarity, with the interviewee's approval of the final text. The accompanying image was generated using GPT-5.6 Sol, guided by the author's careful prompting to represent the themes of the discussion abstractly rather than to depict any real person, place, or event.
In the second issue of our mini-series 'the mainframe', Annamae Muldowney speaks with Rebecca Joskow, a researcher and practitioner in disability inclusion and assistive technology at the Global Disability Innovation Hub (GDI Hub), a research and practice centre driving disability innovation for a fairer world, based at University College London (UCL). They discuss AI, inclusion and the built environment.
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As a consultant at Buro Happold, Erida Bendo advises clients on climate-responsive design at building and urban scales, drawing on microclimate modelling, environmental analysis and spatial data. Alongside this, she leads computational design for the firm's Sustainability team, building the tools and workflows behind evidence-based decisions. She holds a Master's in Architecture from the Polytechnic University of Tirana and a Master's in Computational Design from IAAC in Barcelona, where she has also taught. Over the better part of an hour we covered speed, scepticism, and why the most important climate knowledge still sits between the disciplines. What follows is our conversation, lightly edited.
AM: Before we get to AI, how would you describe your role, and the main climate challenges you're dealing with?
EB: I can describe that best by talking about the two scales the projects I'm involved in revolve around. There's the urban scale, where I mostly consult on climatic aspects of masterplan designs. The thought process begins with understanding which climatic qualities we would like to enforce on outdoor spaces. How to design places that are less prone to heat stress, which support natural ventilation without creating wind hazards during winter is a recurring theme in that sense. So it's mostly about designing with the sun and designing with the wind.
And the same principles are applicable when we talk about the building scale, where I look at the building envelope: how to shape geometries and orientations, what materials are advantageous, how to design the openings in a way that creates good daylight but less overheating? Programme considerations and occupancy, for example what functions these spaces are hosting, during which times of the day or of the year, can also be other decisive factors, which can lower the reliance of buildings on mechanical systems. My interest here is mostly around the effect of passive measures. I use simulations for reassurance, but I wouldn't say they're currently the main bulk of my work, or what I consider the challenge in my work.
AM: So where do you see AI having the biggest impact on your area?
EB: There are two promises of AI: it's supposed to make the work better, faster. On ‘better’ I'm not very sure, but I'm more or less convinced by now that it does make these processes faster. The catch is that it also closes the discussion early. Several years ago, an architect couldn't run a climate simulation themselves, so they'd have to bring specialists in and that meant a real back-and-forth, several of us working on the problem together over time. Now, with all these tools, increasingly AI-assisted, the architect can run a simulation, get a result, feel they've understood the question, and the discussion just stops there. They work faster, yes, but the conversation ends early in the day, before many people have had a chance to shape it together. What I'd expect in the future is fewer people involved in each project, and the design process finished faster.
AM: Could AI make climate and sustainability expertise more accessible to architects without specialist knowledge? And is there a risk it creates an illusion of expertise i.e. that it flattens or dilutes what a specialist really knows?
EB: I definitely agree. I'd compare an AI assistant or any other algorithmic workflow to a human specialist. You have professionals who are very well versed in what they do, and others that are not or who are maybe still figuring things out. So there will be cases where AI supports you and makes things go better and faster, and cases where it creates misunderstanding, specifically that risk of the illusion that you've actually fulfilled the task when you haven't. It's the same as having a specialist in the loop: it depends on the knowledge and the quality of the workflow you're using. And the startup scene is so alive right now, every day there's a new software that can run all these simulations. So with what's out there currently, I do see that effect of creating the illusion. Maybe in the future it won't be the case, but I'd definitely say it's quite a risk.
AM: Is there an area you'd not want to see AI take over?
EB: I can see AI being applied in very specific use cases, but I wouldn't like to see it as something that guides the design process. I'm not a fan of configurators, for example: workflows where you can generate masterplan or layout designs with a mouse click. It goes back to the distinction between holistic technologies and prescriptive technologies, which Ursula Franklin has noted in her work. There's this distinction, prescriptive is when you know every step you need to achieve; and then you have holistic technologies, also linked to crafts, for example pottery makers. And for me, architecture is a craft. So I wouldn't want a design workflow where you press some buttons and you get a finished building out of it, because design as a process isn't only about achieving certain KPIs. It's also about involving communities, involving people whose lives actually get affected by those decisions.
AM: Then where does human insight hold its value?
EB: The role of the climate specialist specifically is changing. If you look at the last century, it was doctors and not architects who were keen on designing ventilation schemes for buildings because of health hazards. With the advent of air conditioning mechanical engineers came into the loop. Then, some years ago, and I'm part of this group, architects with computational design skills started emerging, who got hired in positions where they would carry out building simulations. And now we're at the point where that might not be needed at that scale, because the threshold for using computational tools has been lowered. So I think it's about having the knowledge that sits at the intersection of different topics. It's not only the climate topic, if you look at constructability, or material topics, the building industry is also a very local thing, it differs from country to country. Having the knowledge that sits at the intersection of all of these, and consulting on the best alternative in terms of climate for a given site, given people, that's something I see myself moving towards. And again, it's not a given that simulations always truly reflect what’s really happening out there, the real world physical phenomena. So there's also a process of learning from past projects, from the way you live in your city, the buildings you visit. It's not a discussion that can be merely reduced into a very prescriptive way of following the same steps.
AM: Considering insights, can you ever really hold an AI accountable for what it tells you?
EB: That's the point I'd raise also: can you actually hold it accountable for what it's saying? It takes time to develop trust when working with other professionals, you learn who they are through hearing them speak about who what they do, notice how versed they are on topics, observe how they live their lives and what beliefs they share. I feel that this does not happen while interacting with AI-generated answers. I remain sceptical.
AM: Where would you most want to see AI help on climate specifically?
EB: I'd see it in trying to better understand physical phenomena. What most of the time happens with these climate AI-assisted simulations is that they're built based on results of simulations, they're not built based on what's happening out there. It can be something build on top of simple machine-learning models as well. It's difficult, of course, because you have to get out of your office and try to build a good database. That's where I'd actually be interested in seeing more work done, something that extends the capabilities of simulations, because they're very idealised situations where a lot of approximations happen
AM: As you have mentioned environmental decisions rest on assumptions about a future climate we can't really predict. How do you design for that uncertainty rather than for a single scenario and is AI any help there?
EB: The current discourse revolves around climate models. A lot of climate research centres have simulated different scenarios. But even within that research it's not necessarily clear what we should expect for the same scenario, if you open the datasets from different research centres, you get very different results. So it's not about trying to find what exactly will change, because we won't be able to know that. It's about trying to make more robust buildings. The key word there is robust: don't prepare for a specific future, but for a range of them. As for how AI fits into that, maybe it can help in predicting better the effects of climate change, honestly, I don't really know; for me it's more about changing our approach.
AM: AI has its own environmental footprint. How should the industry hold that tension?
EB: This is not a problem that comes from the building industry itself. In academic discourses architects always put themselves in this position where they have to improve the future, but actually it's a problem about how we live and how we've structured our economies. We're not building because we have skilled architects; we're building because there is an economic profit in doing so. So it's not just about pointing to our individual decisions, pointing to every individual about what they can do best. It's about treating the system as a whole.
AM: If we spoke in ten years, what would tell you AI had a positive influence?
EB: I'd be happy to be in a position where AI takes care of the boring small steps along the way: for example supporting interoperability between different software, different specialities - if I never had to take care of 3D models again. It's this idea of using it for the less interesting bits in the design. And I'd find it dreadful, actually, to see configurators where architects are just people who press buttons.
AM: So would AI have a positive influence if it brought more people into those multidisciplinary, cross speciality conversations, helping everyone hold their own place at the table, and making sure each voice is actually heard?
EB: Definitely. Now that you say it like that, it could also be a kind of project manager, a position that actually makes sure people are heard, since because of many different factors you do have teams that speak about certain aspects more than others. And what I mentioned earlier, the data discussion, just ensuring a building model can be used between different specialists remains to be a challenge. Perhaps AI as a technology could support the emergence of more intuitive tools that support a holistic design process, but then again for me the decisive factor in this discussion remains the professional, if they choose to be aided by algorithmic processes, a level of technological skills would need to exist so that they do not diminish to being just a prompter.
What stayed with me from this conversation is how closely Erida's concerns mirror the ones I meet in my own architectural speciality. I came in expecting the challenges of environmental design to be a world away from inclusive design, and instead we kept landing in the same place: the danger of averaging, of optimising for a single predicted case, be it a person, or a climate, that doesn't really exist, and of mistaking a clean simulation result for the messy reality it stands in for. Her answer, again and again, was to keep people in the loop, the communities a building serves, the specialists around the table, the human judgement that reassures rather than the tool that decides. It's a quietly hopeful position. Not that AI will make us better designers, but that, used with some scepticism and for the right things, it might free us to spend more of our attention on the judgement, the context, the craft.
AI declaration
This interview was conducted in conversation and transcribed with AI-assisted transcription using Microsoft Teams; the transcript was then minimally edited by the author for length and clarity, with the interviewee's approval of the final text. The accompanying image was generated using GPT-5.6 Sol, guided by the author's careful prompting to represent the themes of the discussion abstractly rather than to depict any real person, place, or event.
In the first issue of our new mini-series ‘the mainframe’, Annamae Muldowney interviews Erida Bendo, a Berlin-based architect, computational designer and environmental design specialist. Through expert interviews, the mainframe examines the impact of Artificial Intelligence on design. Each interview focuses on a different intersection, with this issue focusing on what AI can, and cannot, do for climate.
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These difficult questions are not idle speculation. The capabilities of AI are increasing by the day, and our long-held convictions on creativity and design are being questioned [1]. Personally, I have transitioned from working fifteen years as an architect and I now lead AI development, strategy and research at a large architectural practice. I have been observing these tools being used at every project stage and can see areas where they are working and are not. One thing I believe is certain, is that the way we have worked previously is now broken.
Irrespective of whether you're sceptical on AI, unconvinced by what you've seen, or if you've already integrated AI tools in your armoury and are familiar with how they are transforming work from the inside, the context to AI's role in creative work is constantly and rapidly changing. Some are less concerned about the capabilities of AI and more about the consequences for the industry, the values, and above all, its impact on people. These are all valid concerns.
Whether AI can design, and whether AI can be creative, are two different questions. Conflating these questions is where most of the current debate loses its footing. AI's capacity to design, in the sense of performing the tasks that constitute a design process, is largely a question of model capability, and the answer is changing at a pace that is difficult to keep up with. We are arriving at a point where AI agents can begin to orchestrate parts of the process, but without meaningful guidance they have no understanding of why they are doing what they are doing. The creative process is not always linear and is often not compatible with delegation. It does not move through predictable stages with clear milestones. It is continuous, unstructured, at times chaotic, and the understanding that guides it is often something a designer knows intuitively but can often find difficult to articulate, even to themselves. An agent can follow a sequence, but it cannot feel its way through one.
Whether AI can be creative is a question of a different order entirely, one that sits closer to what it means to understand something, to care about it, and to make something in response to that understanding. Creatives are perhaps better placed than anyone to navigate this technological shift, because the answer to the article's question has less to do with what the technology can do and more to do with what we can and will always bring to our work.
Architect and theorist Christopher Alexander devoted much of his career to a question that is simple to ask and very difficult to answer: why do certain places feel deeply, immediately right in a way most people sense but few can put into words. Alexander described design as a search for good fit between form and context, where context meant not a background condition but the full weight of human needs, constraints and relationships easy to miss unless fully understood; 'We are searching for some kind of harmony between two intangibles: a form which we have not yet designed and a context which we cannot properly describe'[2]. His concern was that reducing design thinking to a transferable system passes on the logic but loses the life. Production is a large part of the work we do, but the harder challenge has always lain elsewhere. That gap between systematic knowledge and embodied understanding is exactly what AI now forces us to confront again.
Ethan Mollick, a professor and leading researcher on AI & innovation and its impact on society, describes the form of AI we have ended up with as 'deeply weird in ways that we don't fully understand yet'[3], and warns that treating it like any other tool will always produce less useful outcomes than implementations that embrace that weirdness. My observation in architectural practice, is that the people who are most willing to lean into that strangeness are the ones most capable of influencing design direction, approaching these tools out of deep curiosity [4]. AI only flattens creative work when it is used to seek the average and remove judgement from the process. When designers invite the strange instead, it can lead to something genuinely intriguing.
What AI tools can offer, more than anything else, is freedom. Freedom to explore further, to reach into areas that once felt out of range, to test an idea without the weight of technical limitation slowing the thinking down. Designers are following their curiosity into new territory and finding that the boundaries they once worked within were never as fixed as they seemed. The curious are building their own tools entirely, which is perhaps the purest expression of that freedom, moulding the technology around their imagination rather than the other way around.
We come to the realisation that the process can be delegated, but the understanding behind it cannot. This is not a new concept, and it has always been framed as something existential. CAD was going to be the demise of the art of drawing, CGI was going to hollow out cinema, the sewing machine was going to end fashion as a craft and of course the video killed the radio star. Each time, the creative industry absorbed the tool, expanded its reach and moved onto the next challenging question. The pattern is consistent enough to resist the urge to panic.
So, is creativity still inherently human? My immersion into the space between suggests to me that the answer is yes, and the more capable these tools become, the more important it is to understand why. What AI offers is the removal of friction between a designer and the full scope of their thinking, and while that is incredibly valuable, it is not the same thing as being creative. Creativity is not something the tools produce. It is what we bring to them, the direction we set, the judgements we make, the willingness to keep questioning whether the work is right until we believe that it is.
Alexander asked this question before the tools existed and arrived at the same place: creativity lives in understanding, and understanding remains ours to develop or to neglect. The future belongs to those willing to embrace curiosity.
Disclosure of the use of AI is an important aspect of the work that I do. For transparency: I have used Wisprflow to dictate my thoughts, and Anthropic's Claude Sonnet 4.6 to map these themes for the article concept. The article was edited in collaboration with Cormac from TYPE through phone conversations and document exchanges. The images throughout have been generated with MidJourney. The content and ideas behind the article are my own.
Creativity has long been the human capacity we considered beyond the reach of any machine. Most can agree that Artificial Intelligence (AI) has crossed the threshold of being on the periphery to our work and is now embedding itself into our thinking, our workflows, and our society. As these shifts begin influencing the creative industries, we have to ask: what truly changes, and is creativity still what makes us human?
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