Avoiding "Zombie UI": Katie Dill on Keeping Intent and Craft Alive in the AI Building Boom
Lenny's Podcast
Katie Dill of Stripe opened her talk at the Lenny and Friends Summit with a history lesson about architecture. She argues that the AI boom closely resembles the building boom that followed World War II, and that if builders aren't careful, the result will be the software equivalent of that era's generic buildings. Her question is how to use AI to build products that have care, character, and soul rather than monotony. She answers with three warning signs and four recommendations drawn from Stripe's own work.
The Post-War Building Boom and the Birth of "Zombie Buildings"
Right after World War II, Dill explained, there was a massive building boom, which she called the largest in history. In the US, people were coming home from the war, new technologies and construction methods were available, and buildings went up quickly. The builders of that era borrowed modernism from the 1920s: simple geometries, clean bare surfaces, monochromatic palettes, and no adornment.
Dill was careful to credit the original modernists. Whatever one thinks of the style, she said, its founders had intentionality. There was a point of view behind the Villa Savoye and principles behind the Bauhaus. After the war, though, the urgency to build and the new methods meant the style was copied again and again without that intentionality. "The thinking got thinner and thinner," she said, until all that remained were generic patterns poorly suited to their context.
Her example was banks. Banks once had architecture that signaled trust, reliability, and security. They were replaced by anonymous boxes, a pattern she says has continued to this day. She calls them "zombie buildings." Nothing differentiates them and nothing signals that they were designed for their purpose. In her view they show no care for users, for the people living around them, for context, or for brand.
Three Watch Points for the AI Building Boom
Dill thinks about this often because people are surrounded by such buildings and because we are now in another boom: the AI building boom. "Everybody and their mother can build now," she said, and teams of three can do what used to require teams of thirty. She sees echoes of the post-war period and described three "watch points."
The first is that LLMs are very good at producing the most probable answer. In her framing, that means they are good at telling you what has been popular, what has worked, and what was in style. They are less good at producing something original or specific to your brand and your users' context. To illustrate, she showed a website and asked the audience to guess what the company sold. Software? No, Korean barbecue. She allowed that it was probably a fine website, but said it had no character and no connection to the user or the context in which it would be used.
The second watch point is what she called "the temptation of done." AI makes things feel finished very quickly, often too early. Her example was her own habit of eating a microwave burrito for lunch. She takes a frozen rock out of the freezer and 90 seconds later has lunch. The result is "nearly inedible," she admitted, but she is so taken with the speed that she overlooks serious flaws. She sees the same thing happening with AI. You type a sentence and get an interface with nice rounded corners and drop shadows, and it feels good enough to ship. That polished surface can mislead, she warned. Does it actually solve the problem? Does it differentiate? She noted that the questions Claire Vo raised earlier at the summit apply here.
The third watch point is disposability. Because this work is so quick and easy, it often feels disposable and gets treated that way. People can let responsibility for their decisions slide and stop thinking about the long term, including who will maintain what has been built.
Dill said that together these three forces could produce a world like a real place in Turkey, which she showed as an image of repetitive, undifferentiated construction. The software equivalent is what she calls "zombie UI": patterns that don't fit their context and that are monotonous, vacant, or uncared for.
What Cared-For Software Looks Like
People spend half their waking lives looking at screens, Dill said, so they want software that feels cared for. She gave several small examples. One was the Grok bot's little animation and the sense of life it adds. Another was calendar apps that show the actual date on their tab icon. Users click past this detail all day, she said, but it shows the builder cared about them.
A third example was about understanding context: Stripe's Link agent wallet. According to Dill, it anticipates problems an agent might run into while buying online and includes troubleshooting for them, because the team thought about the user's context even when that user is an agent. For her, these examples show builders who care about their users and put that care into the work, and they reveal a brand's character, or soul. She believes AI can help achieve this, even though it is sometimes the cause of the problem.
Recommendation One: Have a Point of View
Her first recommendation is to have a point of view, because "if you don't, AI will give it for you." Given the first watch point, that borrowed point of view will probably be generic and backward-looking. Teams need to define their brand: what it means to them, what it means to users, who they want to be for those users, and what those users care about. She says this becomes the basis of a team's standards.
She said she would give this advice in any era, but it matters more now. When building is distributed, ownership becomes diffuse. More people contribute to products in more ways than before, and handing decisions over to AI is easy. At Stripe, she said, the company cares a lot about optimism and tries to express it in details large and small: color choices, writing style, the topics it writes about, and products built to support entrepreneurs. Because so many people contribute and so much is changing, being aligned on these details matters.
She described a recent design crit that showed what happens when alignment is missing. The team was reviewing an ad that combined Stripe's parallelogram brand mark with users' visuals. The general idea came through, but some things were off. A cross-functional partner in the room who wanted to move fast and ship asked what Dill called a curious question: what is the quality standard for something made with AI?
Dill's answer was that how something was made shouldn't matter. Users don't care about the method. They care whether the result is good, and so should the team. The discussion produced alignment on what mattered, and the team left with 17 bullet points of improvements: some frost here and there, softer edges, slightly smaller bubbles. According to Dill, the office now uses "Pepsi bubbling" as a verb for this kind of meticulous craft. She said the goal isn't perfection. It is to go one level deeper than what the customer can see, because that craft will reach them. A point of view, in her account, drives both the standards and the work, particularly when agents are involved and so much more happens every day.
How do you develop a point of view? Dill said it comes down to getting very good at noticing. Notice what users need and want, not only what they say. Notice what in the world around you signals good, great, or "meh," both in the products you use and in analogous fields, so that art, science, and a broader perspective can inspire the work. Building taste and understanding raises your own standards, which you then have to scale.
Recommendation Two: Encode Your Standards Into the Machine
Her second recommendation follows from the problem of scale. It would be wonderful, she said, if every person shared the same understanding and made the same decisions. But humans will not be part of every decision from here on. She pointed to interfaces built without a designer present, agents finding and fixing problems overnight, and generative UI created for users in real time.
She says this is why design systems "are having a moment again," although today's system differs from yesterday's. In the past, not everything had to be written down, because a designer was usually present to fill in the gaps. Now systems have to support distributed building and agentic construction, so decisions need to be easier to define. In her words, "the object of design is no longer the screen. It is the system itself."
She illustrated this with Gutenberg. Everyone knows he created the printing press, she said, but fewer people know about the system he built around it. He didn't stop at 26 lowercase and 26 uppercase letters. He made 290 unique characters, including different widths, abbreviations, and ligatures. His goal was fully justified text without the "rivers and lakes" of whitespace that would spoil the finished page. In Dill's reading, he made the system extensible and opinionated enough that machine-made output could feel as good as handmade. That is her target too: "The old system scaled consistency, but the new system needs to scale intent."
She was candid that Stripe is still working this out and has hit snags. The goal is to let builders go from prompt to production almost instantly. Like many companies, Stripe started with an MCP server that understood its design documentation. The results were not good. It wasn't specific enough, it didn't drive the right outcomes, and three people could enter the same prompt and get three different results.
Stripe has since built a CLI on top of its design system. Dill described it as a harness that makes the AI "far more obedient." It lives where builders already work and feeds documentation in at the right time and place, which she said helps avoid context rot. The key difference from earlier design systems is that it includes full templates and flows, not just components and atomic parts. The system therefore knows how the product is supposed to behave and fit together, which makes it much more opinionated than before. Building standards into the means of production, she said, leads to a more coherent product.
She called this only the baseline and quoted Christopher Alexander: "A system can satisfy every rule and still be dead."
Recommendation Three: Refuse to Confuse Done With Good
Her third recommendation starts from the observation that "the filter is gone." Quality filtering used to be built into every stage of product development, even before a project began. A team might have 20 ideas and staff only one, then poke, prod, and prune along the way so products grew fairly methodically. Now a team can build 20 ideas in a week. Dill sees real benefits in this. Teams can skip theoretical meetings about hypothetical products and react to real things. But the filtering that used to happen throughout the process now has to happen after the build, when saying no is much harder.
This is where she sees the role of the editor, which she called "ultra important." She asked the audience who in their organizations looks at the end-to-end experience and judges whether the result is a coherent whole. She called it a new behavior: people have to unlearn that built means done and that done means good.
Her main advice for improving editing skills is to experience the product as a user would. Does it solve the problem? Does it match how the user thinks? Is it coherent? Many things look good in isolation but feel disconnected once they're assembled into a user journey. She added that editing isn't only a yes-or-no decision. It also means asking whether something is fully formed and how to push it to completion. "We don't want done to be the enemy of good."
She cited an article by Nabeel Qureshi on what makes art great. As she summarized it, greatness comes from unexpected details that make you think "I can't believe they thought of that," from deeper meaning beneath the surface, and from continuous themes that tie the work together. Dill said these are things AI is not good at, and knowing those gaps helps people fill them. She paraphrased Qureshi's point that part of what offends people about AI slop is the sense that the details don't matter: the cup is green, but it might as well have been blue. An editor, she said, takes responsibility for every decision, which in many ways means every pixel.
A Case Study: 56 Iterations on the Summit's Opening Animation
Dill's team designed the event itself, and she used its opening animation as an example. Stefan, a member of her team, and his colleagues started with a 3D model of the scene and brought it into AI to help with the animations. She showed the first version. It was fun, it moved nicely, and the individual parts were enjoyable to watch. But looked at pixel by pixel, she said, something felt off. The motion wasn't quite right and was a bit jerky, and you wanted to see more of the scene.
So he kept iterating. After 56 iterations, Dill said, he arrived at something truly beautiful, with more realism and more life. The differences were subtle, but she said you could sense the care. What impressed her most was that without AI he might not have attempted such a complex scene or explored so many viewpoints. It wasn't one-and-done, and it took persistence and scrutiny, but in her view AI opened up the possibility space.
Recommendation Four: Unleash Creativity and Artistry
Dill called her fourth recommendation her favorite. AI can help mass-produce monotony, she acknowledged, but she also called it "the greatest creative catalyst we've ever had." She thinks this matters more now because when everyone is building, differentiating will be both harder and more important.
She argued that today's dominant interfaces, which she listed as chats, charts, and CLIs, are nowhere near the peak of what interaction can be. There is much more to do to make software feel generative, alive, responsive, and dynamic. "We can literally talk to the computers," she said, and urged the audience to branch out and invent new interfaces and new aesthetics. The best practices haven't been written yet, and builders now have the tools to write them. She compared the moment to multi-touch enabling new interactions and the synthesizer enabling new sounds.
She said she sees this happening online: people showing up the Stripe design team with more interesting data visualizations, restaurant websites with character and personality (in contrast to her earlier Korean barbecue example), and people using AI to paint and make art. At Stripe, she said, AI is used to amplify the creative team. The most recent cover of Built to Grow started with a human marbler who produced a series of iterations. The team then used AI to refine the details so the colors and lines were exactly where they wanted them. Dill described this as taking the good judgment of humans and using AI to scale it.
Tactics: Better Inputs, Tougher Scrutiny of Outputs
To make AI a stronger creative partner, Dill offered two tactics. The first is to improve inputs. Prompts should be more specific and point toward your brand and your particular interests. Instead of simply asking for a website for a Korean barbecue restaurant, explain what you believe in, what good looks like, and what you care about. Add the source material you used to build your own standards, and push the model to think in different ways.
The second is to stress-test outputs. Don't fall for "the burrito dilemma." Push one step further, use adversarial agents to critique the work and rough it up, but keep pushing for better yourself.
Culture: "Protect the Strange"
Dill said these tactics are the easy part and the harder part is cultural. Following cookie-cutter patterns is safe and comfortable. Finding something unique that improves the status quo is harder and more impressive. Her advice to team leads was not to simply tell people to use AI, but to give them room to explore: "Protect the strange." AI lowers the cost of creating, and she suggested spending some of those savings on making something special. If AI is only used to make the same things faster, she said, teams miss the most interesting part. AI can "raise the ceiling, not just the floor." What matters most, she said, is intentionality: your point of view, your system, your quality bar, and your ambition, brought to life with AI.
Closing: Gothic Architecture and a Creative Renaissance
Dill closed with a counterexample to post-war modernism: Gothic architecture. She noted that in 1850 John Ruskin wrote extensively about quality and craft and held up Gothic architecture as the epitome of greatness. He observed that no two Gothic buildings were alike, and not even two columns. Each detail was individually crafted and showed the hand and mind of its maker, so the work felt cared for.
That, Dill said, is what users want. They are not impressed that something was animated with Three.js and Blender in 30 minutes. They are impressed when their problems are solved and when clever details show that the builders anticipated their needs and that the brand has character. In this building boom, she concluded, builders can choose not to make the digital equivalent of zombie buildings and can instead make it a creative renaissance, with products that are more powerful and that show the hand and care of the maker.
Good morning.
Right after World War II, there was a massive building boom, the largest in history. In the US, people were coming home from war. There were new technologies, new construction methods, and buildings were going up like gangbusters. The builders of that day drew on the style of modernism from the 1920s. That's really simple geometries, clean, bare surfaces, monochromatic color palettes, and no adornment.
Now, say what you will about modernism, but the founders behind the movement had intentionality. There was a point of view behind the Villa Savoye. There were principles behind the Bauhaus. But after World War II, with urgency to build and new construction methods, the style was essentially copied again and again without intentionality. The thinking got thinner and thinner, and all that was left were generic patterns ill-suited to the context at hand.
For example, we used to have banks that looked like this, signaling trust, reliability, and security. And then we got this. You're all too familiar with this reality because it carried on for decades until this day. Zombie buildings all over the place. Nothing differentiates them. Nothing says this is intentional. This is fit to purpose. They essentially show no care for the user and the inhabitants around it. No care for context or the brand.
I think about this a lot. One, we're surrounded by them. And two, we're in another building boom now. The AI building boom. Everybody and their mother can build now. Teams of three can do what teams of 30 used to be needed for. But there's echoes of the post-war building boom. Similarities that are actually watch points for us. I want to tell you about these watch points and then we'll talk about how we can navigate them together.
First, LLMs are really good at telling you the most probable answer, which essentially means that they're able to tell you what has been or is popular now, what has worked, what was in style. They're less good at telling you what's original or specific to you, your brand, and your users' context. I'll give you an example. Can you guess what this brand sells? Software? No. Korean barbecue. Now this is, I'm sure, a very fine website, but it has no character, no context for the user or the context of the use, which brings me to my second point, the temptation of done.
AI makes things feel finished really, really fast, all too often prematurely. I'll give you an example that I probably shouldn't admit. I have a microwave burrito for lunch all too often. I take the cold, hard rock out of the freezer, plop it on the plate, put it in the microwave. In 90 seconds, I go from hungry to lunch. I'm willing to overlook some pretty serious flaws. It's nearly inedible, but I'm so enamored by the speed of execution.
The same happens with AI. You type in a sentence and boom, you have an interface. There's some nice little corners and maybe some drop shadows and it's good to be done, but an apparently polished state can often be misleading. Does it really solve the problem? Does it really differentiate? All the questions Claire Vu brought up earlier apply here.
And the third watch point, this work is so easy to do, so quick that it often feels disposable and, worse, is treated that way. We sometimes let the responsibility of our decisions fall by the wayside and don't really think about the long term, like who's maintaining this.
Anyways, all three of these watch points can come together to a world that looks a little like this, an actual place in Turkey. If we're not careful, our building boom could end up a little too much like the post-war building boom. The proliferation of patterns ill-suited to the context at hand. Essentially zombie UI. Zombie UI that's monotonous, vacant, or uncared for.
We spend half our waking lives looking at screens. We want software that feels actually cared for. And that means has a little personality, as something as simple as the Grockbot and the little animation and life that it brings. Or even these tiny details like actually having the date in the tab on the calendar. You probably clicked by this all day long, but that shows the builder cared about you. Or the understanding of context like the Link agent wallet. It anticipates the issues that the agent could have while buying online and has the troubleshooting built in because they anticipate the context of the user even when it's an agent. These examples show builders that care about their users and bring that care into the work. It shows the character of the brand and it has soul.
We can do this with AI even though it can sometimes be the culprit. And I have four recommendations on how we can use AI to build products with care and soul.
First one, you have to have a point of view. If you don't, AI will give it for you. And as we've already talked about, that's very likely to be generic and looking at the past. You need to define your brand. What is it for you? What is it for your users? Who do you want to be to them? And what do they care about? This forms the basis of your standards. And I'd say this in any era, but it's all the more important right now. When building is distributed, ownership is diffuse. We're all contributing to the products in more ways than we've been in the past. It's too easy to abdicate to AI.
So, for example, at Stripe, we care a lot about optimism. So we put that into all the details big and small. The colors we choose, the way we write, what we write about, and the products that we ship to try to support entrepreneurs. Now, these details are so important to be aligned on because everybody is contributing and so much is changing.
And I'll give you an example of where that friction can show up if there isn't alignment. We were recently in a design crit looking at an advertisement. So, this is an ad we were working on that shows our brand, the parallelogram, with our users' visuals. And as we were looking at this, we're like, "All right, cool. You kind of get what's happening, but some things are off." And in the room was a cross-functional partner that was looking to move fast and get something shipped. Sure, you can't relate. And we had a discussion because they brought up a really interesting question. What's the quality standard for something made with AI? What a curious question.
Why should that matter how it was made? It doesn't matter to the users how it was made. It matters to them if it's good or not. And so that is the basis of our standards. It's the output at the end of the day that matters and they care if it's good and so should we. So through that discussion, we got alignment on what really matters here and what we're striving for and then walked away with 17 bullet points of improvements to be made. The frost a little bit here and there, softer edges, a bit smaller bubbles. And now we use this term as a verb to mean meticulous craft around the office called Pepsi bubbling. Now, we're not shooting for perfection. We're shooting to go one level deeper than what your customer could see. That meticulous craft will show up for them.
And this point of view really drives the standards and drives the work, especially when there's agents involved. And so much more is happening on a daily basis. So how do you develop it? Frankly, it's all about getting really good at noticing. Notice what your users need and what they want, not just what they say. Notice what about the world around you signals good and great and meh. Really take note of these things in the products that you use, but also in analogous situations so you can be inspired by art, science, and a broader point of view in the work that you create. Building your sense of taste and your understanding of the world around you and what's good and great can really improve your own standards that you will then need to scale.
Which brings me to my second recommendation, which is encode your standards into the machine. It would be a wonderful world if every human had the shared understandings and could make the same decision. But the reality is humans are not going to be a part of all of these decisions going forward. We are seeing interfaces built without a designer in the room. We're seeing agents finding problems and fixing them while we sleep. And we're certainly seeing generative UI built in real time for users.
This is why design systems are having a moment again. And the system of yesterday is different than the system of today. In the past, you didn't have to write everything down because there was always a designer in the room to fill in the blanks. Now, we must enable distributed building and agentic construction and the decisions need to be easier to define. So, the object of design is no longer the screen. It is the system itself.
Now, everybody knows Gutenberg created the first printing press. The lesser-known part of the story is the system he built around it. He didn't create just 26 lowercase and 26 uppercase letters. He created 290 unique characters, different widths, abbreviations, ligatures. And the reason that he did this is because when he typeset it and wanted it to be fully justified, he wanted to make sure that there wouldn't be the weird rivers and lakes of white space that erode the beauty of the final product. Essentially, he was making it both extensible and opinionated enough that it could feel as good as handmade, although it was machine-made. This is what we should be aiming for as well.
Now, the old system scaled consistency, but the new system needs to scale intent. We've been working on this at Stripe and, you know, have hit a few snags along the way. And so, we're figuring it out. But one of the things we want to do is we want to empower builders to go from prompt to production nearly instantaneously. Like many, we started with an MCP that understood our design documentation, but the results were not great. It wasn't specific enough and it wasn't driving the right outcomes. Three different people could put in the same prompt and get three different results.
So since then we've evolved and now we have created a CLI built on our design system. It makes a harness that makes the AI far more obedient. Now it's where the builders are building and it consumes the documentation at the right time and place to avoid context rot. Now, importantly, the big difference between this version and a previous design system is it's not just components and atomic parts, but it's actually full templates and flows. So, the system actually knows what our product is supposed to behave like and how it all comes together. Essentially, it's far more opinionated than it's been in the past. We embed these standards into the means of production, which enables a more coherent product.
But this is just the baseline. Christopher Alexander said, "A system can satisfy every rule and still be dead." This brings me to my third recommendation. Refuse to confuse done with good.
The filter is gone. It used to be. Quality filter was essentially built into every stage of the product development process, even before a project started. We've got 20 ideas and we can only staff one. And then we poked and we prodded and we pruned along the way and products grew somewhat methodically. Well, now we can build 20 ideas in a week. This is awesome in many ways and we can finally skip theoretical meetings talking about hypothetical products and react to the real thing in our hands. But the filtering that used to be throughout the process now needs to happen post-build when it is a lot harder to say no.
This is where the role of an editor comes in and it is ultra important in this day and age. Who is doing this in your organization? Who's looking at the end to end and understanding whether or not we're actually building a whole? It's a new behavior. We have to unlearn that built means done and that done means good.
The most important thing I can say to anybody working on their editing skills is you have to experience it like a user would. Does it actually solve the problem? Is it actually attuned to the way the user thinks about things? Is it actually coherent? A lot of things look good in isolation, but then when you pull it all together into the user journey, it feels a little disconnected. And it's not just about saying yes or no, this is good or it shouldn't go. It's actually about is it even fully formed? How can we push this to completion? We don't want done to be the enemy of good.
A very insightful article by Nabil Koreshi talks about what makes art great. And he says it's about the unexpected details, those little surprising things you're like, "Wow, I can't believe they thought of that." And then the deeper meaning that sits behind the surface or the continuous themes that tie it all together to a whole. These are the things that AI is not great at. But knowing what the gaps are in AI helps us better navigate that and fill these gaps in. As Nabil says, one of the things that so offends us about AI slop is the sense that the details don't matter. The cup is green but may as well have been blue. An editor takes accountability for every decision, which is in many ways every pixel.
I'll give you an example from this event. So my team had the pleasure of designing the event. Stefan worked on this fabulous opening animation. He and the team started with a 3D model of the scene and then they brought it into AI to help with the different animations. Now here's the first version.
It's cool. It's, you know, fun to see the different little parts of it and it's nice the way it moves. But if you scrutinize it pixel by pixel, something feels off, right? That's not quite the way it should move. It's a little jilted. You kind of want to get more of the scene, right? So he did it again and again and again. 56 times, 56 iterations later, he got to something truly beautiful. Now, it's got more realism. It's got a little bit of life and it's subtle differences, but you can sense the care.
Now, one of the things that so impresses me about this is that if he didn't use AI, he may not have built such a complex scene or he may not have tried so many different viewpoints. And while it wasn't one and done and certainly took a lot of wherewithal and scrutiny, AI opened the possibility space.
Now, this brings me to my last and definitely my favorite fourth recommendation. Unleash creativity and artistry. Yes, AI can help us manufacture monotony, but it is also the greatest creative catalyst we've ever had. This matters more now because when everybody and their mother is building something, it is going to be harder and more important to differentiate.
And the interfaces of today, chats, charts, CLIs. No way is that the epitome of great interactions in the modern era. There is so much more we can do and so much more we should do to make things feel generative, alive, responsive, dynamic. We can literally talk to the computers. So, let's branch out. It is time to invent new interfaces. It is time to invent new aesthetics. The best practices have not been written yet. We get to do that and now we have the tools to do it.
It's just like when multi-touch made it possible to do wholly new interactions or the synthesizer allowed us to create totally new sounds. AI is allowing all sorts of new creativity. And we're seeing so much of this online. We're even seeing folks showing up the Stripe design team with way more interesting data viz. And we're seeing websites for restaurants with character and personality and people using AI to paint and create art themselves. It is a really interesting time.
And at Stripe, we're using AI to essentially amplify the abilities of the creative team. Our most recent cover for Built to Grow, it was created by a human marbler who worked on these stunning iterations. And then we used AI to fine-tune the details ever further to ensure we had the colors and the lines in all the right places. It was basically taking the good judgment of the humans and helping us scale it to make something truly stunning.
Now, to make AI a stronger creative partner, there's a couple of things I recommend. One, improve your inputs. You want more specificity going in the direction of your brand, your unique interests. So, put specific prompts. Don't just say, "Hey, I need a website for my Korean barbecue," but this is what
I believe in. This is what good is. This is what we care about." Add your source material that you're using to build your own standards with. Help make it think in different ways. And then stress your outputs. Don't get tempted by the burrito dilemma. Always push a step further and then of course use adversarial agents to help you critique it and bang it up a little bit, but you yourself should always be pushing for better.
Now, these are just tactics. The much harder thing is definitely cultural. It is easy to follow cookie cutter patterns. It's safe and cozy, but it is much more impressive and much harder to find something unique that improves the status quo. So, if you're leading a team, don't just tell them to use AI, but give them room to explore. Protect the strange. AI lowers the cost to create. Let's spend some of that savings on making something truly special.
If we only use AI to make the things that we already make just faster, then we are definitely missing out on the most interesting part. AI can help us raise the ceiling, not just the floor. The most important thing is to be very intentional. It's your point of view. It's your system. It's your quality bar and your ambition that you will want to bring to life with AI.
In total contrast to the post-war building boom and modernism design was Gothic architecture. In 1850, John Ruskin wrote a lot about quality and craft and highlighted Gothic architecture as the epitome of great. He noticed that no Gothic building was alike. Frankly, not even one column was alike the other. Each detail was uniquely crafted and showed the unique hand and mind of the maker behind it. It felt truly cared for.
This is what our users want. They're not impressed if we animated something with Three.js and Blender in 30 minutes. They are impressed by us solving their problems and clever touches in the details that show we anticipated their needs and that our brand has some character behind it.
We have the choice in this building boom to not make the digital equivalent of zombie buildings. We can make this a creative renaissance. So, let's make some products that are more powerful and show the hand and care of the maker. Thanks everybody.
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