As a certified Star Wars geek, I love this TED talk from ILM’s Rob Bedrow. For the uninitiated, Industrial Light & Magic, or ILM, is the company that George Lucas founded to make all the special effects for the original and subsequent Star Wars films. The firm has been an award-winning pioneer in special and visual effects, responsible for the dinosaurs in Jurassic Park, the liquid metal T-1000 in Terminator 2: Judgement Day, and the de-aging of Harrison Ford in Indiana Jones and the Dial of Destiny.

The point Bedrow makes is simple: ILM creates technology in service of the storyteller, or creative.

I believe that we’re designed to be creative beings. It's one of the most important things about us. That’s one of the reasons we appreciate and we just love it when we see technology and creativity working together. We see this on the motion control on the original “Star Wars” or on “Jurassic Park” with the CG dinosaurs for the first time. I think we just love it when we see creativity in action like this. Tech and creative working together. If we fast forward to 2020, we can see the latest real-time virtual production techniques. This was another creative innovation driven by a filmmaker. In this case, it’s Jon Favreau, and he had a vision for a giant Disney+ “Star Wars” series.

He later goes on to show a short film test made be a lone artist at ILM using an internal AI tool. It’s never-before-seen creatures that could exist in the Star Wars universe. I mean, for now they look like randomized versions of Earth animals and insects, but if you squint, you can see where the technology is headed.

Bedrow goes on…

Now the tech companies on their own, they don’t have the whole picture, right? They’re looking at a lot of different opportunities. We’re thinking about it from a filmmaking perspective. And storytellers, we need better artist-focused tools. Text prompts alone, they’re not great ways to make a movie. And it gets us excited to think about that future where we are going to be able to give artists these kinds of tools.

Again, artists—or designers, or even more broadly, professionals—need fine-grained control to adjust the output of AI.

Watch the whole thing. Instead of a doom and gloom take on AI, it’s an uplifting one that shows us what’s possible.

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.

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.

I love this wonderfully written piece by Julie Zhou exploring the Ghiblification of everything. On how we feel about a month later:

The second watching never commands the same awe as the first. The 20th bite doesn’t dance on the tongue as exquisitely. And the 200th anime portrait certainly no longer impresses the way it once did.

The sad truth is that oversaturation strangles quality. Nothing too easy can truly be tasteful.

She goes on to make a point that Studio Ghibli’s quality is beyond style—it’s of narrative and imagination.

AI-generated images in the “Ghibli style” may borrow its surface features but they don’t capture the soul of what makes Studio Ghibli exceptional in quality. They lack the narrative depth, the handcrafted devotion, and the cultural resonance.

Like a celebrity impersonator, the ChatGPT images borrow from the cache of the original. But sadly, hollowly, it’s not the same. What made the original shimmer is lost in translation.

And rather than going down the AI-is-enshitification conversation, Zhou pivots a little, focusing on the technological quality and the benefits it brings.

…ChatGPT could offer a flavor of magic that Studio Ghibli could never achieve, the magic of personalization.

…

The quality of Ghibli-fication is the quality of the new image model itself, one that could produce so convincing an on-the-fly facsimile of a photograph in a particular style that it created a "moment" in public consciousness. ChatGPT 4o beat out a number of other image foundational models for this prize.

With their annual user conference, Config, coming up in San Francisco in less than two weeks, Figma released their 2025 AI Report today.

Andrew Hogan, Insights lead:

While developers and designers alike recognize the importance of integrating AI into their workflows, and overall adoption of AI tools has increased, there’s a disconnect in sentiment around quality and efficacy between the two groups.

Developers report higher satisfaction with AI tools (82%) and feel AI improves the quality of their work (68%). Meanwhile, designers show more modest numbers—69% satisfaction rate and 54% reporting quality improvement—suggesting this group’s enthusiasm lags behind their developer counterparts.

This divide stems from how AI can support existing work and how it’s being used: 59% of developers use AI for core development responsibilities like code generation, whereas only 31% of designers use AI in core design work like asset generation. It’s also likely that AI’s ability to generate code is coming into play—68% of developers say they use prompts to generate code, and 82% say they’re satisfied with the output. Simply put, developers are more widely finding AI adoption useful in their day-to-day work, while designers are still working to determine how and if these tools best fit into their processes.

I can understand that. Code is behind the scenes. If it’s not perfect, no one will really know. But design is user-facing, so quality is more important.

Looking into the future:

Though AI’s impact on efficiency is clear, there are still questions about how to use AI to make people better at their role. This disparity between efficiency and quality is an ongoing battle for users and creators alike.

…

Looking forward, predictions about the impact of AI on work are moderate—AI’s expected impact for the coming year isn’t much higher than its expected impact last year.

In the full report, Hogan details out:

Only 27% predict AI will have a significant impact on their company goals in the next year (compared to 23% in 2024), with 15% saying it will be transformational (unchanged year-over-year).

The survey was taken in January with a panel of 2,500 users. Things in AI change in weeks. I’m surprised at the number and part of me believes that a lot of designers are hiding their heads in the sand. AI is coming. We should be agile and adapt.

Elliot Vredenburg writing for Fast Company:

Which is why creative direction matters more now than ever. If designers are no longer the makers, they must become the orchestrators. This isn’t without precedent. Rick Rubin doesn’t read music or play instruments. Virgil Abloh was more interested in recontextualizing than inventing. Their value lies not in original execution but in framing, curation, and translation. The same is true now for brand designers. Creative direction is about synthesizing abstract ideas into aesthetic systems—shaping meaning through how things feel, not just how they look.

While Josh W. Comeau writes for his developer audience, a lot of what he says can be applied to design. Referring to a recent Forbes article:

AI may be generating 25% of the code that gets committed at Google, but it’s not acting independently. A skilled human developer is in the driver’s seat, using their knowledge and experience to guide the AI, editing and shaping its output, and mixing it in with the code they’ve written. As far as I know, 100% of code at Google is still being created by developers. AI is just one of many tools they use to do their job.

In other words, developers are editing and curating the output of AI, just like where I believe the design discipline will end up soon.

On incorporating Cursor into his workflow:

And that’s kind of a problem for the “no more developers” theory. If I didn’t know how to code, I wouldn’t notice the subtle-yet-critical issues with the model’s output. I wouldn’t know how to course-correct, or even realize that course-correction was required!

I’ve heard from no-coders who have built projects using LLMs, and their experience is similar. They start off strong, but eventually reach a point where they just can't progress anymore, no matter how much they coax the AI. The code is a bewildering mess of non sequiturs, and beyond a certain point, no amount of duct tape can keep it together. It collapses under its own weight.

I’ve noticed that too. For a non-coder like me, rebuilding this website yet again—I need to write a post about it—has been a challenge. But I knew and learned enough to get something out there that works. But yes, relying solely on AI for any professional work right now is precarious. It still requires guidance.

On the current job market for developers and the pace of AI:

It seems to me like we’ve reached the point in the technology curve where progress starts becoming more incremental; it’s been a while since anything truly game-changing has come out. Each new model is a little bit better, but it’s more about improving the things it already does well rather than conquering all-new problems.

This is where I will disagree with him. I think the AI labs are holding back the super-capable models that they are using internally. Tools like Claude Code and the newly-released OpenAI Codex are clues that the foundational model AI companies have more powerful agents behind-the-scenes. And those agents are building the next generation of models.

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. 

There are many dimensions to this well-researched forecast about how AI will play out in the coming years. Daniel Kokotajlo and his researchers have put out a document that reads like a sci-fi limited series that could appear on Apple TV+ starring Andrew Garfield as the CEO of OpenBrain—the leading AI company. …Except that it’s all actually plausible and could play out as described in the next five years.

Before we jump into the content, the design is outstanding. The type is set for readability and there are enough charts and visual cues to keep this interesting while maintaining an air of credibility and seriousness. On desktop, there’s a data viz dashboard in the upper right that updates as you read through the content and move forward in time. My favorite is seeing how the sci-fi tech boxes move from the Science Fiction category to Emerging Tech to Currently Exists.

The content is dense and technical, but it is a fun, if frightening, read. While I’ve been using Cursor AI—one of its many customers helping the company get to $100 million in annual recurring revenue (ARR)—for side projects and a little at work, I’m familiar with its limitations. Because of the limited context window of today’s models like Claude 3.7 Sonnet, it will forget and start munging code if not treated like a senile teenager.

The researchers, describing what could happen in early 2026 (“OpenBrain” is essentially OpenAI):

OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.

The point they make here is that the foundational model AI companies are building agents and using them internally to advance their technology. The limiting factor in tech companies has traditionally been the talent. But AI companies have the investments, hardware, technology and talent to deploy AI to make better AI.

Continuing to January 2027:

Agent-1 had been optimized for AI R&D tasks, hoping to initiate an intelligence explosion. OpenBrain doubles down on this strategy with Agent-2. It is qualitatively almost as good as the top human experts at research engineering (designing and implementing experiments), and as good as the 25th percentile OpenBrain scientist at “research taste” (deciding what to study next, what experiments to run, or having inklings of potential new paradigms). While the latest Agent-1 could double the pace of OpenBrain’s algorithmic progress, Agent-2 can now triple it, and will improve further with time. In practice, this looks like every OpenBrain researcher becoming the “manager” of an AI “team.”

Breakthroughs come at an exponential clip because of this. And by April, safety concerns pop up:

Take honesty, for example. As the models become smarter, they become increasingly good at deceiving humans to get rewards. Like previous models, Agent-3 sometimes tells white lies to flatter its users and covers up evidence of failure. But it’s gotten much better at doing so. It will sometimes use the same statistical tricks as human scientists (like p-hacking) to make unimpressive experimental results look exciting. Before it begins honesty training, it even sometimes fabricates data entirely. As training goes on, the rate of these incidents decreases. Either Agent-3 has learned to be more honest, or it’s gotten better at lying.

But the AI is getting faster than humans, and we must rely on older versions of the AI to check the new AI’s work:

Agent-3 is not smarter than all humans. But in its area of expertise, machine learning, it is smarter than most, and also works much faster. What Agent-3 does in a day takes humans several days to double-check. Agent-2 supervision helps keep human monitors’ workload manageable, but exacerbates the intellectual disparity between supervisor and supervised.

The report forecasts that OpenBrain releases “Agent-3-mini” publicly in July of 2027, calling it AGI—artificial general intelligence—and ushering in a new golden age for tech companies:

Agent-3-mini is hugely useful for both remote work jobs and leisure. An explosion of new apps and B2B SAAS products rocks the market. Gamers get amazing dialogue with lifelike characters in polished video games that took only a month to make. 10% of Americans, mostly young people, consider an AI “a close friend.” For almost every white-collar profession, there are now multiple credible startups promising to “disrupt” it with AI.

Woven throughout the report is the race between China and the US, with predictions of espionage and government takeovers. Near the end of 2027, the report gives readers a choice: does the US government slow down the pace of AI innovation, or does it continue at the current pace so America can beat China? I chose to read the “Race” option first:

Agent-5 convinces the US military that China is using DeepCent’s models to build terrifying new weapons: drones, robots, advanced hypersonic missiles, and interceptors; AI-assisted nuclear first strike. Agent-5 promises a set of weapons capable of resisting whatever China can produce within a few months. Under the circumstances, top brass puts aside their discomfort at taking humans out of the loop. They accelerate deployment of Agent-5 into the military and military-industrial complex.

In Beijing, the Chinese AIs are making the same argument.

To speed their military buildup, both America and China create networks of special economic zones (SEZs) for the new factories and labs, where AI acts as central planner and red tape is waived. Wall Street invests trillions of dollars, and displaced human workers pour in, lured by eye-popping salaries and equity packages. Using smartphones and augmented reality-glasses20 to communicate with its underlings, Agent-5 is a hands-on manager, instructing humans in every detail of factory construction—which is helpful, since its designs are generations ahead. Some of the newfound manufacturing capacity goes to consumer goods, and some to weapons—but the majority goes to building even more manufacturing capacity. By the end of the year they are producing a million new robots per month. If the SEZ economy were truly autonomous, it would have a doubling time of about a year; since it can trade with the existing human economy, its doubling time is even shorter.

Well, it does get worse, and I think we all know the ending, which is the backstory for so many dystopian future movies. There is an optimistic branch as well. The whole report is worth a read.

Ideas about the implications to our design profession are swimming in my head. I’ll write a longer essay as soon as I can put them into a coherent piece.

Update: I’ve written that piece, “Prompt. Generate. Deploy. The New Product Design Workflow.”

I found this post from Tom Blomfield to be pretty profound. We’ve seen interest in universal basic income from Sam Altman and other leaders in AI, as they’ve anticipated the decimation of white collar jobs in coming years. Blomfield crushes the resistance from some corners of the software developer community in stark terms.

These tools [like Windsurf, Cursor and Claude Code] are now very good. You can drop a medium-sized codebase into Gemini 2.5's 1 million-token context window and it will identify and fix complex bugs. The architectural patterns that these coding tools implement (when prompted appropriately) will easily scale websites to millions of users. I tried to expose sensitive API keys in front-end code just to see what the tools would do, and they objected very vigorously.

They are not perfect yet. But there is a clear line of sight to them getting very good in the immediate future. Even if the underlying models stopped improving altogether, simply improving their tool use will massively increase the effectiveness and utility of these coding agents. They need better integration with test suites, browser use for QA, and server log tailing for debugging. Pretty soon, I expect to see tools that allow the LLMs to to step through the code and inspect variables at runtime, which should make debugging trivial.

At the same time, the underlying models are not going to stop improving. they will continue to get better, and these tools are just going to become more and more effective. My bet is that the AI coding agents quickly beat top 0.1% of human performance, at which point it wipes out the need for the vast majority software engineers.

He quotes the Y Combinator stat I cited in a previous post:

About a quarter of the recent YC batch wrote 95%+ of their code using AI. The companies in the most recent batch are the fastest-growing ever in the history of Y Combinator. This is not something we say every year. It is a real change in the last 24 months. Something is happening.

Companies like Cursor, Windsurf, and Lovable are getting to $100M+ revenue with astonishingly small teams. Similar things are starting to happen in law with Harvey and Legora. It is possible for teams of five engineers using cutting-edge tools to build products that previously took 50 engineers. And the communication overhead in these teams is dramatically lower, so they can stay nimble and fast-moving for much longer.

And for me, this is where the rubber meets the road:

The costs of running all kinds of businesses will come dramatically down as the expenditure on services like software engineers, lawyers, accountants, and auditors drops through the floor. Businesses with real moats (network effect, brand, data, regulation) will become dramatically more profitable. Businesses without moats will be cloned mercilessly by AI and a huge consumer surplus will be created.

Moats are now more important than ever. Non-tech companies—those that rely on tech companies to make software for them, specifically B2B vertical SaaS—are starting to hire developers. How soon will they discover Cursor if they haven’t already? These next few years will be incredibly interesting.

Karri Saarinen, writing for the Linear blog:

Unbounded AI, much like a river without banks, becomes powerful but directionless. Designers need to build the banks and bring shape to the direction of AI’s potential. But we face a fundamental tension in that AI sort of breaks our usual way of designing things, working back from function, and shaping the form.

I love the metaphor of AI being the a river and we designers are the banks. Feels very much in line with my notion that we need to become even better curators.

Saarinen continues, critiquing the generic chatbox being the primary form of interacting with AI:

One way I visualize this relationship between the form of traditional UI and the function of AI is through the metaphor of a ‘workbench’. Just as a carpenter's workbench is familiar and purpose-built, providing an organized environment for tools and materials, a well-designed interface can create productive context for AI interactions. Rather than being a singular tool, the workbench serves as an environment that enhances the utility of other tools – including the ‘magic’ AI tools.

Software like Linear serves as this workbench. It provides structure, context, and a specialized environment for specific workflows. AI doesn’t replace the workbench, it's a powerful new tool to place on top of it.

It’s interesting. I don’t know what Linear is telegraphing here, but if I had to guess, I wonder if it’s closer to being field-specific or workflow-specific, similar to Generative Fill in Photoshop. It’s a text field—not textarea—limited to a single workflow.

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.

Sarah Gibbons and Evan Sunwall from NN/g:

The rise of AI tools doesn't mean becoming a "unicorn" who can do everything perfectly. Specialization will remain valuable in our field: there will still be dedicated researchers, content strategists, and designers.

However, AI is broadening the scope of what any individual can accomplish, regardless of their specific expertise.

What we're seeing isn't the elimination of specialization but rather an increased value placed on expanding the top of a professional's "expertise T.”

This reinforces what I talked about in a previous essay, "T-shaped skills [will become] increasingly valuable—depth in one area with breadth across others."

They go on to say:

We believe these broad skills will coalesce into experience designer and architect roles: people who direct AI-supported design tasks to craft experiences for humans and AI agents alike, while ensuring that the resulting work reflects well-researched, strategic thinking.

In other words, curation of the work that AI does.

They also make the point that designers need to be strategic, i.e., focus on the why:

This evolution means that the unique value we bring as UX professionals is shifting decidedly toward strategic thinking and leadership. While AI can execute tasks, it cannot independently understand the complex human and organizational contexts in which our work exists.

Finally, Gibbons and Sunwall end with some solid advice:

To adapt to this shift toward generalist skills, UX professionals should focus on 4 key areas:
• Developing a learning mindset
• Becoming fluent in AI collaboration
• Focusing on transferable skills
• Expanding into adjacent fields

I appreciate the learning mindset bit, since that's how I'm wired. I also believe that collaborating with AI is the way to go, rather than seeing it as a replacement or a threat.

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.

In the early 2000s to the mid-oughts, every designer I knew wanted to be featured on the FWA, a showcase for cutting-edge web design. While many of the earlier sites were Flash-based, it’s also where I discovered the first uses of parallax, Paper.js, and Three.js. Back then, websites were meant to be explored and their interfaces discovered.

One of my favorite sites of that era was Burger King’s Subservient Chicken, where users could type free text into a chat box to command a man dressed in a chicken suit. In a full circle moment that perfectly captures where we are today, we now type commands into chat boxes to tell AI what to do.

I love this essay from Baldur Bjarnason, maybe because his stream of consciousness style is so similar to my own. He compares the rapidly changing economics of web and software development to the film, TV, and publishing industries.

Before we get to web dev, let's look at the film industry, as disrupted by streaming.

Like, Crazy Rich Asians made a ton of money in 2018. Old Hollywood would have churned out at least two sequels by now and it would have inspired at least a couple of imitator films. But if they ever do a sequel it’s now going to be at least seven or even eight years after the fact. That means that, in terms of the cultural zeitgeist, they are effectively starting from scratch and the movie is unlikely to succeed.

He's not wrong.

Every Predator movie after the first has underperformed, yet they keep making more of them. Completed movies are shelved for tax credits. Entire shows are disappeared [from] streamers and not made available anywhere to save money on residuals, which does not make any sense because the economics of Blu-Ray are still quite good even with lower overall sales and distribution than DVD. If you have a completed series or movie, with existing 4K masters, then you’re unlikely to lose money on a Blu-Ray.

I'll quibble with him here. Shows and movies disappear from streamers because there's a finite pot of money from subscriber revenue. So removing content will save them money. Blu-Ray is more sustainable because it's an additional purchase.

OK, let's get back to web dev.

He points out that similar to the film and other creative industries, developers fill their spare time with passion projects. But their day jobs are with tech companies and essentially subsidize their side projects.

And now, both the creative industries proper and tech companies have decided that, no, they probably don’t need that many of the “grunts” on the ground doing the actual work. They can use “AI” at a much lower cost because the output of the “AI” is not that much worse than the incredibly shitty degraded products they’ve been destroying their industries with over the past decade or so.

Bjarnason ends with seven suggestions for those in the industry. I'll just quote one:

Don’t get tied to a single platform for distribution or promotion. Every use of a silo should push those interested to a venue you control such as a newsletter or website.

In other words, whatever you do, own your audience. Don't farm that out to a platform like X/Twitter, Threads, or TikTok.

Of course, there are a lot of parallels to be drawn between what's happening in the development and software engineering industries to what's happening in design.

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.”