WEBVTT

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Good morning.

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Right after World War II, there was a

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massive building boom, the largest in

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history. In the US, people were coming

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home from war. There were new

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technologies, new construction methods,

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and buildings were going up like gang

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busters. The builders of that day
drew on the style of modernism from the

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1920s.

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That's really simple geometries, clean,

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bare surfaces, monochromatic color

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palettes, and no adornment. Now, say

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what you will about modernism,
but the founders behind the movement had

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intentionality.
There was a point of view behind the

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Villa Savois. There were principles

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behind the Bow House. But after World

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War II, with urgency to build and new

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construction methods, the style was

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essentially copied again and again

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without intentionality.
The thinking got thinner and thinner,

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and all that was left were generic

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patterns ills suited to the context at

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hand. For example, we used to have banks

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that looked like this, signaling trust,

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reliability, and security.

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And then we got this.

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You're all too familiar with this

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reality because it carried on for

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decades until this day. Zombie buildings

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all over the place. Nothing

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differentiates them. Nothing says this

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is intentional. This is fit to purpose.

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They essentially show no care for the

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user and the habitants around it. No

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care for context or the brand. I think

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about this a lot. One, we're surrounded

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by them. And two, we're in another

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building boom now. The AI building boom.

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Everybody and their mother can build

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now. Teams of three can do what teams of

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30 used to be needed for. But there's

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echoes of the post-war building boom.

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Similarities that are actually watch
points for us. I want to tell you about

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these watch points and then we'll talk

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about how we can navigate them together.

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First, LLMs are really good at telling

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you the most probable answer, which

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essentially means that they're able to

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tell you what has been or is popular

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now, what has worked, what was in style.

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They're less good at telling you what's

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original or specific to you, your brand,

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and your users context. I'll give you
an example. Can you guess what this brand

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sells?

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Software? No. Korean barbecue.

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Now this is I'm sure a very fine website

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but it has no character no context for

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the user or the context of the use which

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brings me to my second point than

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temptation of done. AI makes things feel

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finished really really fast all too

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often prematurely. I'll give you an

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example that I probably shouldn't admit.

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I have a microwave burrito for lunch all

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too often. I take the cold, hard rock
out of the freezer, plop it on the

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plate, put it in the microwave. In 90

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seconds, I go from hungry to lunch. I'm

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willing to overlook some pretty serious

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flaws. It's nearly inedible, but I'm so

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enamored by the speed of execution.

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The same happens with AI. You type in a

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sentence and boom, you have an

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interface. There's some nice little

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corners and maybe some drop shadows and

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it's good to be done, but an apparently

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polished state can often be misleading.

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Does it really solve the problem?
Does it really differentiate? All the

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questions Claire Vu brought up earlier

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apply here. And the third watch point,

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this work is so easy to do, so quick

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that it often feels disposable and worst

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is treated that way. We sometimes let

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the responsibility of our decisions fall
by the wayside and don't really think

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about the long term, like who's
maintaining this. Anyways, all three of

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these watch points can come together to

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a world that looks a little like this,

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an actual place in Turkey. If we're not

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careful, our building boom could end
up a little too much like the post-war

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building boom. The proliferation of

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patterns ills suited to the context at

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hand. essentially zombie UI. Zombie UI

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that's monotonous, vacant, or uncared

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for. We spend half our waking lives

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looking at screens. We want software

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that feels actually cared for. And that

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means has a little personality
as something as simple as the Grockbot and

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the little animation and life that it

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brings. Or even these tiny details like

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actually having the date in the tab
on the calendar. You probably clicked by

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this all day long, but that shows the

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builder cared about you. Or the

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understanding of context like the link

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agent wallet. It anticipates the issues

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that the agent could have while buying

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online and has the troubleshooting built

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in because they anticipate the context

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of the user even when it's an agent.

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These examples show builders that are
care about their users and bring that

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care into the work. It shows the

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character of the brand and it has soul.

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We can do this with AI even though it

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can sometimes be the culprit. And I have

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four recommendations on how we can use

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AI to build products with care and soul.

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First one, you have to have a point of

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view. If you don't, AI will give it for

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you. And as we've already talked about,
that's very likely to be generic and

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looking at the past. You need to define

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your brand. What is it for you? What is

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it for your users? Who do you want to be

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to them? And what do they care about?

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This forms the basis of your standards.

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And I'd say this in any era, but it's

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all the more important right now. When

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building is distributed, ownership
is diffuse. We're all contributing to the

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products in more ways than we've been in

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the past. It's too easy to abdicate to

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AI. So, for example, at Stripe, we care

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a lot about optimism. So we put that

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into all the details big and small. The

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colors we choose, the way we write, what

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we write about, and the products that we

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ship to try to support entrepreneurs.

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Now, these details are so important to

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be aligned on because everybody
is contributing and so much is changing.

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And I'll give you an example of where
that friction can show up if there isn't

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alignment. We were recently in a design
crit looking at an advertisement. So,

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this is an ad we were working on that

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shows our brand, the parallelogram,
with our users visuals. And as we were

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looking at this, we're like, "All right,
cool. You kind of get what's happening,

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but some things are off." And in the room
was a cross functional partner that

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was looking to move fast and get
something shipped. Sure, you can't

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relate. And we had a discussion because
they brought up a really interesting

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question. What's the quality standard
for something made with AI? What a curious

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question. Why should
that matter how it was made?

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It doesn't matter to the users how it
was made. It matters to them if it's

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good or not. And so that is the basis of

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our standards. It's the output at the

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end of the day that matters and they
care if it's good and so should we. So

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through that discussion, we've got
alignment on what really matters here

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and what we're striving for and then
walked away with 17 bullet points of

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improvements to be made. the frost a

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little bit here and there, softer edges,

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a bit smaller bubblers. And now we use

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this term as a verb to mean meticulous

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craft around the office called Pepsi
bubbling. Now, we're not shooting for

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perfection. We're shooting to go one
level deeper than what your customer

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could see. That meticulous craft will

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show up for them. And this point of view

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really drives the standards and drives
the work, especially when there's agents

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involved. And so much more is happening

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on a daily basis. So how do you develop

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it? Frankly, it's all about getting

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really good at noticing. Notice what

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your users need and what they want, not

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just what they say. Notice what about

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the world around you signals good and

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great and meh. really take note of these

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things in the products that you use, but

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also in analogous situations so you can

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be inspired by art, science, and a
broader point of view in the work that

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you create. Building your sense of taste
and your understanding of the world

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around you and what's good and great can

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really improve your own standards that

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you will then need to scale. Which

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brings me to my second recommendation,

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which is encode your standards into the

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machine. It would be a wonderful
world if every human had the shared

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understandings and could make the same
decision. But the reality is is humans

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are not going to be a part of all
of these decisions going forward. We are

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seeing interfaces built without
a designer in the room. We're seeing

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agents finding problems and fixing them

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while we sleep. And we're certainly

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seeing generative UI built in real time

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for users. This is why design systems

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are having a moment again. And the

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system of yesterday is different than

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the system of today. In the past,
you didn't have to write everything down

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because there was always a designer
in the room to fill in the blanks. Now, we

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must enable distributed building and

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agentic construction and the decisions

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need to be easier to define. So, the

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object of design is no longer the

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screen. It is the system itself. Now,

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everybody knows Gutenberg created the

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first printing press. The lesserk known

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part of the story is the system he built

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around it. He didn't create just 26

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lowercase and 26 uppercase letters. He

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created 290 unique characters, different

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widths, abbreviations, liatures. And the

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reason that he did this is because when
he type set it and wanted it to be fully

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justified, he wanted to make sure that
there wouldn't be the weird rivers and

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lakes of whites space that erode
the beauty of the final product.

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Essentially, he was making it both
extensible and opinionated enough that

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it could feel as good as handmade,

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although it was machine-made. This is

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what we should be aiming for as well.

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Now, the old system scaled consistency,

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but the new system needs to scale intent.
We've been working on this at

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Stripe and, you know, have hit a few
snags along the way. And so, we're we're

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figuring it out. But one of the things

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we want to do is we want to empower

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builders to go from prompt to production

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nearly instantaneously. Like many, we

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started with an MCP that understood
our design documentation, but the results

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were not great. it wasn't specific
enough and it wasn't driving the right

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outcomes. Three different people could

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put in the same prompt and get three

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different results. So since then we've

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evolved and now we have created a CLI
built on our design system. It makes a

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harness that makes the AI far more
obedient. Now it's where the builders

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are building and it consumes the

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documentation at the right time and

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place to avoid context rot. Now,
importantly, the big difference between

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this version and a previous design
system is it's not just components and

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atomic parts, but it's actually full

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templates and flows. So, the system
actually knows what our product is

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supposed to behave like and how it all
comes together. Essentially, it's far

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more opinionated than it's been in the
past. We embed these standards into the

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means of production, which enables a

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more coherent product. But this is just

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the baseline. Christopher Alexander

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said, "A system can satisfy every rule

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and still be dead." This brings me to my

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third recommendation.

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Refuse to confuse done with good.

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The filter is gone. It used to be.

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Quality filter was essentially
built into every stage of the product

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development process, even before a

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project started. We've got 20 ideas and

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we can only staff one. And then we poked

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and we prodded and we pruned along
the way and products grew somewhat

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methodically.

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Well, now we can build 20 ideas in a

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week. This is awesome in many ways and

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we can finally skip theoretical meetings
talking about hypothetical products and

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react to the real thing in our hands.

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But the filtering that used to be

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throughout the process now needs to

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happen post build when it is a lot

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harder to say no. This is where the role

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of an editor comes in and it is ultra

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important in this day and age. Who
is doing this in your organization? Who's

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looking at the end to end
and understanding whether or not we're

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actually building a whole? It's a new

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behavior. We have to unlearn that built

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means done and that done means good. The

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most important thing I can say
to anybody working on their editing skills

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is you have to experience it like a user

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would. Does it actually solve the

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problem? Is it actually attuned to the

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way the user thinks about things? Is it

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actually coherent? A lot of things look
good in isolation, but then when you

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pull it to all together into the user

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journey, it feels a little disconnected

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and it's not just about saying yes or no,
this is good or it shouldn't go.

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It's actually about is it even fully

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formed? How can we push this to

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completion? We don't want done to be the

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enemy of good. A very insightful article

00:14:16.720 --> 00:14:19.757
by Nabil Koreshi talks about what makes

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art great. And he says it's about
the unexpected details, those little

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surprising things you're like, "Wow,
I can't believe they thought of that." And

00:14:28.160 --> 00:14:32.557
then the deeper meaning that sits behind
the surface or the continuous themes

00:14:32.640 --> 00:14:34.477
that tie it all together to a whole.

00:14:34.560 --> 00:14:36.397
These are the things that AI is not

00:14:36.480 --> 00:14:39.917
great at. But knowing what the gaps are

00:14:40.000 --> 00:14:43.037
in AI help us better navigate that and

00:14:43.120 --> 00:14:47.037
fill these gaps in. As Nibil says, one

00:14:47.120 --> 00:14:49.197
of the things that so offends us about

00:14:49.280 --> 00:14:51.997
AI slop is the sense that the details

00:14:52.080 --> 00:14:55.837
don't matter. The cup is green but may

00:14:55.920 --> 00:14:58.797
as well have been blue. An editor takes

00:14:58.880 --> 00:15:01.677
accountability for every decision, which

00:15:01.760 --> 00:15:05.277
is in many ways every pixel. I'll give

00:15:05.360 --> 00:15:08.237
you an example from this event. So my

00:15:08.320 --> 00:15:10.317
team had the pleasure of designing the

00:15:10.400 --> 00:15:12.877
event. Stefan worked on this fabulous

00:15:12.960 --> 00:15:15.357
opening animation.

00:15:15.440 --> 00:15:17.917
He and the team started with a 3D model

00:15:18.000 --> 00:15:22.717
of the scene and then they brought
it into AI to help with the different

00:15:22.800 --> 00:15:24.317
animations.

00:15:24.400 --> 00:15:27.597
Now here's the first version.

00:15:27.680 --> 00:15:31.357
It's cool. It's, you know, fun to see
the different little parts of it and

00:15:31.440 --> 00:15:35.837
it's nice the way it moves. But if
you scrutinate, scrutinize it pixel by

00:15:35.920 --> 00:15:37.837
pixel, something feels off, right?

00:15:37.920 --> 00:15:39.117
That's not quite the way it should move.

00:15:39.200 --> 00:15:40.717
It's a little jilted. You kind of want

00:15:40.800 --> 00:15:43.757
to get more of the scene, right? So he

00:15:43.840 --> 00:15:46.157
did it again

00:15:46.240 --> 00:15:48.317
and again

00:15:48.400 --> 00:15:52.557
and again. 56 times,

00:15:52.640 --> 00:15:55.597
56 iterations later, he got to something

00:15:55.680 --> 00:15:58.237
truly beautiful. Now, it's got more

00:15:58.320 --> 00:16:00.477
realism. It's got a little bit of life

00:16:00.560 --> 00:16:03.117
and it's subtle differences, but you can

00:16:03.200 --> 00:16:05.517
sense the care.

00:16:05.600 --> 00:16:07.997
Now, what one of the things that so uh

00:16:08.080 --> 00:16:10.877
impresses me about this is that if he

00:16:10.960 --> 00:16:13.357
didn't use AI, he may not have built

00:16:13.440 --> 00:16:18.397
such a complex scene or he may not
have tried so many different viewpoints.

00:16:18.480 --> 00:16:20.317
And while it wasn't one and done and

00:16:20.400 --> 00:16:22.077
certainly took a lot of wherewithal and

00:16:22.160 --> 00:16:25.437
scrutiny, AI opened the possibility

00:16:25.520 --> 00:16:28.557
space. Now, this brings me to my last

00:16:28.640 --> 00:16:30.797
and definitely my favorite fourth

00:16:30.880 --> 00:16:32.717
recommendation.

00:16:32.800 --> 00:16:36.957
Unleash creativity and artistry. Yes, AI

00:16:37.040 --> 00:16:39.837
can help us manufacture monotony, but it

00:16:39.920 --> 00:16:44.237
is also the greatest creative
catalyst we've ever had.

00:16:44.320 --> 00:16:47.997
This matters more now because when
everybody in their mother is building

00:16:48.080 --> 00:16:50.557
something, it is going to be harder and

00:16:50.640 --> 00:16:53.677
more important to differentiate.

00:16:53.760 --> 00:16:57.437
and the interfaces of today, chats,

00:16:57.520 --> 00:17:02.077
charts, CLIs. No way is that the epitome

00:17:02.160 --> 00:17:05.197
of great interactions in the modern era.

00:17:05.280 --> 00:17:07.037
There is so much more we can do and so

00:17:07.120 --> 00:17:09.437
much more we should do to make things

00:17:09.520 --> 00:17:13.357
feel generative, alive, responsive,

00:17:13.440 --> 00:17:16.157
dynamic. We can literally talk to the

00:17:16.240 --> 00:17:19.197
computers. So, let's branch out. It is

00:17:19.280 --> 00:17:22.237
time to invent new interfaces. It is

00:17:22.320 --> 00:17:25.117
time to invent new aesthetics. The best

00:17:25.200 --> 00:17:30.237
practices have not been written yet. We
get to do that and now we have the tools

00:17:30.320 --> 00:17:34.477
to do it. It's just like when
multi-touch made it possible to do

00:17:34.560 --> 00:17:36.397
wholly new interactions or the

00:17:36.480 --> 00:17:38.717
synthesizer allowed us to create totally

00:17:38.800 --> 00:17:41.917
new sounds. AI is allowing all sorts of

00:17:42.000 --> 00:17:44.237
new creativity. And we're seeing so much

00:17:44.320 --> 00:17:46.797
of this online. We're even seeing folks

00:17:46.880 --> 00:17:49.117
showing up the Stripe design team with

00:17:49.200 --> 00:17:51.597
way more interesting data viz. And we're

00:17:51.680 --> 00:17:53.917
seeing websites for restaurants with

00:17:54.000 --> 00:17:56.877
character and personality and people

00:17:56.960 --> 00:17:59.677
using AI to paint and create art

00:17:59.760 --> 00:18:02.717
themselves. It is a really interesting

00:18:02.800 --> 00:18:05.837
time. And at Stripe, we're using AI to

00:18:05.920 --> 00:18:08.717
essentially amplify the abilities of the

00:18:08.800 --> 00:18:11.517
creative team. Our more most recent

00:18:11.600 --> 00:18:14.557
cover for Built to Grow, it was created

00:18:14.640 --> 00:18:17.917
by a human marbler who worked on these

00:18:18.000 --> 00:18:21.597
stunning iterations. And then we used AI

00:18:21.680 --> 00:18:24.237
to fine-tune the details ever further to

00:18:24.320 --> 00:18:26.157
ensure we had the colors and the lines

00:18:26.240 --> 00:18:29.117
in all the right places.

00:18:29.200 --> 00:18:33.517
It was basically taking the good
judgment of the humans and helping us

00:18:33.600 --> 00:18:38.157
scale it to make something truly stunning.
Now, to make AI a stronger

00:18:38.240 --> 00:18:39.757
creative partner, there's a couple of

00:18:39.840 --> 00:18:43.197
things I recommend. One, improve your

00:18:43.280 --> 00:18:46.157
inputs. You want more specity going in

00:18:46.240 --> 00:18:49.037
the direction of your brand, your unique

00:18:49.120 --> 00:18:51.997
interests. So, put specific prompts.

00:18:52.080 --> 00:18:56.237
Don't just say, "Hey, I need a website
for my Korean barbecue, but this is what

00:18:56.320 --> 00:19:01.437
I believe in. This is what good is. This
is what we care about." Add your source

00:19:01.520 --> 00:19:03.117
material that you're using to build your

00:19:03.200 --> 00:19:05.837
own standards with. Help make it think

00:19:05.920 --> 00:19:09.357
in different ways. And then stress your

00:19:09.440 --> 00:19:11.757
outputs. Don't get tempted by the

00:19:11.840 --> 00:19:14.557
burrito dilemma. always push a step

00:19:14.640 --> 00:19:17.277
further and then of course use

00:19:17.360 --> 00:19:21.917
adversarial agents to help you critique
it and bang it up a little bit, but you

00:19:22.000 --> 00:19:26.717
yourself should always be pushing for
better. Now, these are just tactics. The

00:19:26.800 --> 00:19:30.237
much harder thing is definitely cultural.

00:19:30.320 --> 00:19:32.717
It is easy to follow cookie cutter

00:19:32.800 --> 00:19:36.797
patterns. It's safe and cozy, but it is

00:19:36.880 --> 00:19:39.757
much more impressive and much harder to

00:19:39.840 --> 00:19:42.477
find something unique that improves the

00:19:42.560 --> 00:19:45.117
status quo. So, if you're leading a

00:19:45.200 --> 00:19:47.917
team, don't just tell them to use AI,

00:19:48.000 --> 00:19:50.637
but give them room to explore.

00:19:50.720 --> 00:19:54.477
Protect the strange. AI lowers the cost

00:19:54.560 --> 00:19:58.957
to create. Let's spend some of that
savings on making something truly

00:19:59.040 --> 00:20:03.197
special. If we only use
AI to make the things

00:20:03.280 --> 00:20:07.597
that we already make just faster,
then we are definitely missing out on the

00:20:07.680 --> 00:20:10.157
most interesting part.

00:20:10.240 --> 00:20:13.117
AI can help us raise the ceiling, not

00:20:13.200 --> 00:20:16.077
just the floor. The most important thing

00:20:16.160 --> 00:20:19.597
is to be very intentional.

00:20:19.680 --> 00:20:22.957
It's your point of view. It's your

00:20:23.040 --> 00:20:27.437
system. It's your quality bar and your

00:20:27.520 --> 00:20:29.117
ambition

00:20:29.200 --> 00:20:30.957
that you will want to bring to life with

00:20:31.040 --> 00:20:34.477
AI. In total contrast to the post-war

00:20:34.560 --> 00:20:37.197
building boom and modernism design was

00:20:37.280 --> 00:20:39.517
Gothic architecture.

00:20:39.600 --> 00:20:43.117
In 1850, John Ruskin wrote a lot about

00:20:43.200 --> 00:20:45.997
quality and craft and highlighted Gothic

00:20:46.080 --> 00:20:50.077
architecture as the epitome of great. He

00:20:50.160 --> 00:20:52.237
noticed that no Gothic building was

00:20:52.320 --> 00:20:55.117
alike. Frankly, not even one column was

00:20:55.200 --> 00:20:58.237
alike the other. Each detail was

00:20:58.320 --> 00:21:00.717
uniquely crafted and showed the unique

00:21:00.800 --> 00:21:04.477
hand and mind of the maker behind it. It

00:21:04.560 --> 00:21:07.517
felt truly cared for.

00:21:07.600 --> 00:21:10.717
This is what our users want. They're not

00:21:10.800 --> 00:21:13.037
impressed if we animated something with

00:21:13.120 --> 00:21:16.397
3JS and Blender in 30 minutes.

00:21:16.480 --> 00:21:18.797
They are impressed by us solving their

00:21:18.880 --> 00:21:21.997
problems and clever touches in the

00:21:22.080 --> 00:21:26.637
details that show we anticipated
their needs and that our brand has some

00:21:26.720 --> 00:21:31.117
character behind it. We have the choice
in this building boom to not make the

00:21:31.200 --> 00:21:33.597
digital equivalent of zombie buildings.

00:21:33.680 --> 00:21:36.877
We can make this a creative renaissance.

00:21:36.960 --> 00:21:38.557
So, let's make some products that are

00:21:38.640 --> 00:21:41.677
more powerful and show the hand and care

00:21:41.760 --> 00:21:44.687
of the maker. Thanks everybody.

00:21:44.770 --> 00:21:46.790
[applause]