My former colleague from Organic, Christian Haas—now ECD at YouTube—has been experimenting with AI video generation recently. He’s made a trilogy of short films called AI Jobs.

You can watch part one above 👆, but don’t sleep on parts two and three.

Haas put together a “behind the scenes” article explaining his process. It’s fascinating and I’ll want to play with video generation myself at some point.

I started with a rough script, but that was just the beginning of a conversation. As I started generating images, I was casting my characters and scouting locations in real time. What the model produced would inspire new ideas, and I would rewrite the script on the fly. This iterative loop continued through every stage. Decisions weren't locked in; they were fluid. A discovery made during the edit could send me right back to "production" to scout a new location, cast a new character and generate a new shot. This flexibility is one of the most powerful aspects of creating with Gen AI.

It’s a wonderful observation Haas has made—the workflow enabled by gen AI allows for more creative freedom. In any creative endeavor where the production of the final thing is really involved and utilizes a significant amount of labor and materials, be it a film, commercial photography, or software, planning is a huge part. We work hard to spec out everything before a crew of a hundred shows up on set or a team of highly-paid engineers start coding. With gen AI, as shown here with Google’s Veo 3, you have more room for exploration and expression.

UPDATE: I came across this post from Rory Flynn after I published this. He uses diagrams to direct Veo 3.

Luke Wroblewski, writing in his blog:

Across several of our companies, software development teams are now "out ahead" of design. To be more specific, collaborating with AI agents (like Augment Code) allows software developers to move from concept to working code 10x faster. This means new features become code at a fast and furious pace.

When software is coded this way, however, it (currently at least) lacks UX refinement and thoughtful integration into the structure and purpose of a product. This is the work that designers used to do upfront but now need to "clean up" afterward. It's like the development process got flipped around. Designers used to draw up features with mockups and prototypes, then engineers would have to clean them up to ship them. Now engineers can code features so fast that designers are ones going back and cleaning them up.

This is what I’ve been secretly afraid of. That we would go back to the times when designers were called in to do cleanup. Wroblewski says:

Instead of waiting for months, you can start playing with working features and ideas within hours. This allows everyone, whether designer or engineer, an opportunity to learn what works and what doesn’t. At its core rapid iteration improves software and the build, use/test, learn, repeat loop just flipped, it didn't go away.

Yeah, or the feature will get shipped this way and be stuck this way because startups move fast and move on.

My take is that as designers, we need to meet the moment and figure out how to build design systems and best practices into the agentic workflows our developer counterparts are using.

This is a really well-written piece that pulls the AI + design concepts neatly together. Sharang Sharma, writing in UX Collective:

As AI reshapes how we work, I’ve been asking myself, it’s not just how to stay relevant, but how to keep growing and finding joy in my craft.

In my learning, the new shift requires leveraging three areas
1. AI tools: Assembling an evolving AI design stack to ship fast
2. AI fluency: Learning how to design for probabilistic systems
3. Human-advantage: Strengthening moats like craft, agency and judgment to stay ahead of automation

Together with strategic thinking and human-centric skills, these pillars shape our path toward becoming an AI-native designer.

Sharma connects all the crumbs I’ve been dropping this week:

• Designing for AI interfaces
• Designing for control
• Understanding orchestration and agents
• Prompt and context engineering
• Being AI native

If you want an introduction on how to use Cursor as a designer, here’s a must-watch video. It's just over half-an-hour long and Elizabeth Lin goes through several demos in Cursor.

Cursor is much more advanced than the AI prompt-to-code tools I've covered here before. But with it, you'll get much more control because you're building with actual code. (Of course, sigh, you won't have sliders and inputs for controlling design.)

Nick Babich writing for UX Planet:

Because AI design and code generators quickly take an active part in the design process, it’s essential to understand how to make the most of these tools. If you’ve played with Cursor, Bolt, Lovable, or v0, you know the output is only as good as the input.

Well said, especially as prompting is the primary input for these AI tools. He goes on to enumerate his five parts to a good prompt. Worth a quick read.

I was recently featured on the Design of AI podcast to discuss my article that pit eight AI prompt-to-code tools head to head. We talked through the list but I also offered a point of view on where I see the gap.

Arpy Dragffy and Brittany Hobbs close out the episode this way (emphasis mine):

So it's great that Roger did that analysis and that evaluation. I honestly am a bit shocked by those results. Again, his ranking was that Subframe was number one, Onlook was two, v0 number three, Tempo number four. But again, if you look at his matrix, only two of the tools scored over 70 out of 100 and only one of the tools he could recommend. And this really shines a dark light on AI products and their maturity right now. But I suspect that this comes down to the strategy that was used by some of these products. If you go to them, almost every single one of them is actually a coding tool, except the two that scored the highest.

Onlook, its headline is “The Cursor for Designers.” So of course it's a no brainer that makes a lot of sense. That's part of their use cases, but nonetheless it didn't score that good in his matrix.

The top scoring one from his list Subframe is directly positioned to designers. The title is “Design meet code.” It looks like a UI editor. It looks like the sort of tool that designers wish they had. These tools are making it easier for product managers to run research programs, to turn early prototypes and ideas into code to take code and really quick design changes. When you need to make a change to a website, you can go straight into one of these tools and stand up the code.

Listen on Apple Podcasts and Spotify.

For as long as I can remember, I’ve been fascinated by how television shows and movies are made. I remember the specials ABC broadcast about the making of The Empire Strikes Back and other Lucasfilm movies like the Indiana Jones series. More recently—especially with the advent of podcasts—I’ve loved listening to how show runners think about writing their shows. For example, as soon as an episode of Battlestar Galactica aired, I would rewatch it with Ronald D. Moore’s commentary. These days, I‘m really enjoying the official The Last of Us podcast because it features commentary from both Craig Mazin and Neil Druckmann.

Anyway, thinking about personas as characters from TV shows and movies and using screenwriting techniques is right up my alley. Laia Tremosa for the IxDF:

Hollywood spends millions to bring characters to life. UX design teams sometimes spend weeks… only to make personas no one ever looks at again. So don’t aim for personas that look impressive in a slide deck. Aim for personas that get used—in design reviews, product decisions, and testing plans.

Be the screenwriter. Be the director. Be the casting agent.

A lot of chatter in the larger design and development community has been either “AI is the coolest” or “AI is shite and I want nothing to do with it.”

Tobias van Schneider puts it plainly:

AI is here to stay.

Resistance is futile. Doesn't matter how we feel about it. AI has arrived, and it's going to transform every industry, period. The ship has sailed, and we're all along for the ride whether we like it or not. Not using AI in the future is the equivalent to not using the internet. You can get away with it, but it's not going to be easy for you.

He goes on to argue that craftspeople have been affected the most, not only by AI, but by the proliferation of stock and templates:

The warning signs have been flashing for years. We've witnessed the democratization of design through templates, stock assets, and simplified tools that turned specialized knowledge into commodity. Remember when knowing Photoshop guaranteed employment? Those days disappeared years ago. AI isn't starting this fire, it's just pouring gasoline on it. The technical specialist without artistic vision is rapidly becoming as relevant as a telephone operator in the age of smartphones. It's simply not needed anymore.

But he’s not all doom and gloom.

If the client could theoretically do everything themselves with AI, then why hire a designer?

Excellent question. I believe there are three reasons to continue hiring a designer:

1. Clients lag behind. It'll takes a few years before they fully catch up and stop hiring creatives for certain tasks, at which point creatives have caught up on what makes them worthy (beyond just production output).

2. Clients famously don't know what they want. That's the primary reason to hire a designer with a vision. Even with AI at their fingertips, they wouldn't know what instructions to give because they don't understand the process.

3. Smart clients focus on their strengths and outsource the rest. If I run a company I could handle my own bookkeeping, but I'll hire someone. Same with creative services. AI won't change that fundamental business logic. Just because I can, doesn't mean I should.

And finally, he echoes the same sentiment that I’ve been saying (not that I’m the originator of this thought—just great minds think alike!):

What differentiates great designers then?

The Final Filter: taste & good judgment

Everyone in design circles loves to pontificate about taste, but it's always the people with portfolios that look like a Vegas casino who have the most to say. Taste is the emperor's new clothes of the creative industry, claimed by all, possessed by few, recognized only by those who already have it.

In other words, as designers, we need to lean into our curation skills.

Dan Maccarone:

If users don’t trust the systems we design, that’s not a PM problem. It’s a design failure. And if we don’t fix it, someone else will, probably with worse instincts, fewer ethics, and a much louder bullhorn.

UX is supposed to be the human layer of technology. It’s also supposed to be the place where strategy and empathy actually talk to each other. If we can’t reclaim that space, can’t build products people understand, trust, and want to return to, then what exactly are we doing here?

It is a long read but well worth it.

There has been an explosion of AI-powered prompt-to-code tools within the last year. The space began with full-on integrated development environments (IDEs) like Cursor and Windsurf. These enabled developers to use leverage AI assistants right inside their coding apps. Then came a tools like v0, Lovable, and Replit, where users could prompt screens into existence at first, and before long, entire applications.

A couple weeks ago, I decided to test out as many of these tools as I could. My aim was to find the app that would combine AI assistance, design capabilities, and the ability to use an organization’s coded design system.

While my previous essay was about the future of product design, this article will dive deep into a head-to-head between all eight apps that I tried. I recorded the screen as I did my testing, so I’ve put together a video as well, in case you didn’t want to read this.

Product design is going to change profoundly within the next 24 months. If the AI 2027 report is any indication, the capabilities of the foundational models will grow exponentially, and with them—I believe—will the abilities of design tools.

The TL;DR of the report is this: companies like OpenAI have more advanced AI agent models that are building the next-generation models. Once those are built, the previous generation is tested for safety and released to the public. And the cycle continues. Currently, and for the next year or two, these companies are focusing their advanced models on creating superhuman coders. This compounds and will result in artificial general intelligence, or AGI, within the next five years. 

Haiyan Zhang gives us another way of thinking about AI—as material, like clay, paint, or plywood—instead of a tool. I like that because it invites exploration:

When we treat AI as a design material, prototyping becomes less about refining known ideas — and more about expanding the space of what’s possible. It’s messy, surprising, sometimes frustrating — but that’s what working with any material feels like in its early days.

Clay resists. Wood splinters. AI misinterprets.

But in that material friction, design happens.

The challenge ahead isn’t just to use AI more efficiently — it’s to foster a culture of design experimentation around it. Like any great material, AI won’t reveal its potential through control, but through play, feedback, and iteration.

I love this metaphor. It’s freeing.

In a recent podcast with partners at startup incubator Y Combinator, Jared Friedman, citing statistics from a survey with their current batch of founders says, “[The] crazy thing is one quarter of the founders said that more than 95% of their code base was AI generated, which is like an insane statistic. And it’s not like we funded a bunch of non-technical founders. Like every one of these people is highly tactical, completely capable of building their own product from scratch a year ago…”

A comment they shared from founder Leo Paz reads, “I think the role of Software Engineer will transition to Product Engineer. Human taste is now more important than ever as codegen tools make everyone a 10x engineer.”

While vibe coding—the new term coined by Andrej Karpathy about coding by directing AI—is about leveraging AI for programming, it’s a window into what will happen to the software development lifecycle as a whole and how all the disciplines, including product management and design will be affected.

A tall man with curly, graying hair and a bushy mustache sits across from a woman with a very slight smile in a dimly lit room. There’s pleasant, calming music playing. He’s eager with anticipation to learn about his Outie. He’s an Innie who works on the “severed” floor at Lumon. He’s undergone a surgical procedure that splits his work self from his personal self. This is the premise of the show Severance on Apple TV+.

Ms. Casey, the therapist:

I’ve had Matthew Butterick’s Practical Typography website/ebook bookmarked since I discovered it over ten years ago. It’s making the rounds again, and I think it’s a good reminder that we are all “professional writers” as he describes:

When we think of “professional writers” we probably think of novelists, screenwriters, or journalists. But the programmer, the scientist, the lawyer—and you, if your work depends on presenting written ideas—all deserve to be called professional writers.

But as professional writers, we do more than write. We edit, we format, we print, we generate PDFs, we make web pages. More than ever, we’re responsible for delivering the written word to our readers. So we’re not just writers—we’re publishers.

Typography is the visual component of the written word. Thus, being a publisher of the written word necessarily means being a typographer.

He’s right. As much of our work is in producing documents and content, we are publishers. Here are a few of my favorite pages:

• Straight and curly quotes (I have a similar post from 2009)
• One space between sentences
• Em sizing

This book reminds me of a couple of seminal books from the early 1990s: The Mac Is Not a Typewriter by Robin Williams and Stop Stealing Sheep by Erik Spiekermann and E. M. Ginger. The former is how I learned all the basics, back when I was designing my high school’s newspaper. The latter is more comprehensive, going deeper into how type works conceptually. These three are all essential resources for any designer.

Fabricio Teixeira and Caio Braga, in their annual The State of UX report:

Despite all the transformations we’re seeing, one thing we know for sure: Design (the craft, the discipline, the science) is not going anywhere. While Design only became a more official profession in the 19th century, the study of how craft can be applied to improve business dates back to the early 1800s. Since then, only one thing has remained constant: how Design is done is completely different decade after decade. The change we’re discussing here is not a revolution, just an evolution. It’s simply a change in how many roles will be needed and what they will entail. “Digital systems, not people, will do much of the craft of (screen-level) interaction design.”

Scary words for the UX design profession as it stares down the coming onslaught of AI. Our industry isn’t the first one to face this—copywriters, illustrators, and stock photographers have already been facing the disruption of their respective crafts. All of these creatives have had to pivot quickly. And so will we.

Teixeira and Braga remind us that “Design is not going anywhere,” and that “how Design is done is completely different decade after decade.”

I recently read a post on Threads in which Stephen Beck wonders why the New York Times needs an external advertising agency when it already has an award-winning agency in-house. You can read the back-and-forth in the thread itself, but I think Nina Alter’s reply sums it up best:

Creatives need to be free to bring new perspectives. Drink other kool-aid. That’s much of the value in agencies.

This all got me thinking about the differences between working in-house and at an agency. As a designer who began my career bouncing from agency to agency before settling in-house, I’ve seen both sides of this debate firsthand. Many of my designer friends have had similar paths. So, I’ll speak from that perspective. It’s biased and probably a little outdated since I haven’t worked at an agency since 2020, and that was one that I owned.

I think the best path for a young designer is to work for agencies at the beginning of their careers. It’s sort of like casually dating when you first start dating. You quickly experience a bunch of different types of people. You figure out what your preferences are. You make mistakes. You learn a lot about your own strengths and weaknesses. And most importantly, you grow. This is all training for eventually settling down and investing in a long-term relationship with a partner.

I’ve always been a maker at heart—someone who loves to bring ideas to life. When AI exploded, I saw a chance to create something new and meaningful for solo designers. But making Griffin AI was only half the battle…

Birth of an Idea

About a year ago, a few months after GPT-4 was released and took the world by storm, I worked on several AI features at Convex. One was a straightforward email drafting feature but with a twist. We incorporated details we knew about the sender—such as their role and offering—and the email recipient, as well as their role plus info about their company’s industry. To accomplish this, I combined some prompt engineering and data from our data providers, shaping the responses we got from GPT-4.

Playing with this new technology was incredibly fun and eye-opening. And that gave me an idea. Foundational large language models (LLMs) aren’t great yet for factual data retrieval and analysis. But they’re pretty decent at creativity. No, GPT, Claude, or Gemini couldn’t write an Oscar-winning screenplay or win the Pulitzer Prize for poetry, but it’s not bad for starter ideas that are good enough for specific use cases. Hold that thought.