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.

You might not know his name—I sure didn’t—but you’ll surely recognize his illustration style that came to embody the style du jour of the 1960s and ’70s. Robert E. McGinnis has died at the age of 99. The New York Times has an obituary:

Robert E. McGinnis, an illustrator whose lusty, photorealistic artwork of curvaceous women adorned more than 1,200 pulp paperbacks, as well as classic movie posters for “Breakfast at Tiffany’s,” featuring Audrey Hepburn with a cigarette holder, and James Bond adventures including “Thunderball,” died on March 10 at his home in Greenwich, Conn. He was 99.

…

Mr. McGinnis’s female figures from the 1960s and ’70s flaunted a bold sexuality, often in a state of semi undress, whether on the covers of detective novels by John D. MacDonald or on posters for movies like “Barbarella” (1968), with a bikini-clad Jane Fonda, or Bond films starring Sean Connery and Roger Moore.

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.”

In the 1980s and ’90s, Emigre was a prolific powerhouse. The company started out as a magazine in the mid-1980s, but quickly became a type foundry as the Mac enabled desktop publishing. As a young designer in San Francisco who started out in the ’90s, Zuzana Licko and Rudy VanderLans were local heroes (they were based across the Bay in Berkeley). From 1990–1999 they churned out 37 typefaces for a total of 157 fonts. And in that decade, they expanded their influence by getting into music, artists book publishing, and apparel. More than any other design brand, they celebrated art and artists.

Here is a page from a just-released booklet (with a free downloadable PDF) showcasing their fonts from the Nineties.

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.

Jay Hoffman, from his excellent The History of the Web site:

1995 is a fascinating year. It’s one of the most turbulent in modern history. 1995 was the web’s single most important inflection point. A fact that becomes most apparent by simply looking at the numbers. At the end of 1994, there were around 2,500 web servers. 12 months later, there were almost 75,000. By the end of 1995, over 700 new servers were being added to the web every single day.

That was surely a crazy time…

Elizabeth Goodson, writing for It’s Nice That:

The cynicism our current moment inspires appears to be, regrettably, universal. For millennials, who watched the better-world-by-design ship go down in real time, it’s hard-earned. We saw the idealist fantasy of creative autonomy, social impact, and purpose-driven work slowly unravel over the past decade, and are now left holding the bag. Gen Z designers have the same pessimism, but arrived at it from a different angle. They’re entering the field already skeptical, shaped by a job market in freefall and constant warnings of their own obsolescence. But the result is the same: an industry full of people who care deeply, but feel let down.

Sounds very similar to what Gen X-ers are facing in their careers too. I think it’s universal for nearly all creative careers today.

Travis Bumgarner:

The people of Mexico, without asking anybody, chose bright colors to paint their houses. I fell in love. Every time I pass by a colorful house, I feel inspired.

It’s color palettes based on random houses in Mexico.

On 4/4, Marvel released a wonderful teaser poster for their upcoming movie, The Fantastic Four: First Steps. This will be the fourth iteration of the first family of comics on film. There was an unreleased Roger Corman-produced movie from 1994, with a fascinating history.

As a longtime Apple fanboy, it's a little hard for me to appreciate the visual design of Windows—Microsoft is a nemesis, if you will. But I will tip my hat to the design practitioners there who've made the company finally pay attention to design.

Side note, reminds me of a story about what Steve Jobs once told me when I was designing the welcome animation for Mac OS X.

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.

Related to the NYT article about Gen X-ers in creative industries that I posted yesterday, graphic design historian Steven Heller explores what happened with advertising—specifically print—creative in the 2000s.

Advertising did not change when the Times Square ball fell at the stroke of midnight on Jan. 1, 2000, but the industry began its creative decline in the early 2000s. Here are several indicators to support this claim: For one, the traditional print outlets for advertisements, notably magazines and newspapers, sharply declined in numbers (some turning to digital-only) during the late 1990s and early 2000s. Major advertisers were cutting print budgets and earmarking creative talent for television work. TV had already plucked away many of the most imaginative ad-people during the preceding decades, and print slipped lower down on the hierarchical ladder.

He continues:

The work of 1960s and 1970s “mad men” smothered conventional establishment agencies at Art Directors Club award competitions, spawning the innovative Big Idea creative dynamic where exceptional art directors and copywriters made witty, ironic and suggestive slogans and visuals. But, by the early 2000s, these teams started to cede their dominance with, among the other social factors, the death of many national print magazines and the failure of television networks to retain large audiences in the face of cable.

In my first couple of years in design school, I was enamored with advertising. It seemed so glamorous to be making ads that appeared in glossy magazines and on TV. I remember visiting the offices of an agency in San Francisco—the name escapes me—and just loving the vibe and the potential. After graduation and into my career, I would brush up against ad agencies, collaborating with them on the pieces my design company was working on. Sometimes it was with FCB on Levi's retail work, or BBDO for Mitsubishi Motors digital campaigns. I ended up working for a small ad agency in 2010, PJA Advertising & Marketing, doing B2B ads. It was fun and I learned a lot, but it wasn't glamorous.

Anyway, back to Heller's article…it's reinforcing the idea that our—potentially Boomers, Gen Xers, and even Millennials—mental model of the creative and media world must change due to reality. And we must pivot our careers or be left behind.

Steven Kurtz, writing for The New York Times:

For many of the Gen X-ers who embarked on creative careers in the years after [Douglas Coupland's Generation X] was published, lessness has come to define their professional lives.

If you entered media or image-making in the ’90s — magazine publishing, newspaper journalism, photography, graphic design, advertising, music, film, TV — there’s a good chance that you are now doing something else for work. That’s because those industries have shrunk or transformed themselves radically, shutting out those whose skills were once in high demand.

My first assumption was that Kurtz was writing about AI and how it's taking away all the creative jobs. Instead, he weaves together a multifactorial illustration about the diminishing value of commercial creative endeavors like photography, music, filmmaking, copywriting, and design.

“My peers, friends and I continue to navigate the unforeseen obsolescence of the career paths we chose in our early 20s,” Mr. Wilcha said. “The skills you cultivated, the craft you honed — it’s just gone. It’s startling.”

Every generation has its burdens. The particular plight of Gen X is to have grown up in one world only to hit middle age in a strange new land. It’s as if they were making candlesticks when electricity came in. The market value of their skills plummeted.

It's more than AI, although certainly, that is top of everyone's mind these days. Instead, it's also stock photography and illustrations, graphic templates, the consolidation of ad agencies, the revolutionary rise of social media, and the tragic fall of traditional media.

Similar shifts have taken place in music, television and film. Software like Pro Tools has reduced the need for audio engineers and dedicated recording studios; A.I., some fear, may soon take the place of actual musicians. Streaming platforms typically order fewer episodes per season than the networks did in the heyday of “Friends” and “ER.” Big studios have slashed budgets, making life for production crews more financially precarious.

Earlier this year, I cited Baldur Bjarnason's essay about the changing economics of web development. As an opening analogy, he referenced the shifting landscape of film and television.

Born in 1973, I am squarely in Generation X. I started my career in the design and marketing industry just as the internet was taking off. So I know exactly what the interviewees of Kurtz's article are facing. But by dogged tenacity and sheer luck, I've been able to pivot and survive. Am I still a graphic designer like I was back in the mid-1990s? Nope. I'm more of a product designer now, which didn't exist 30 years ago, and which is a subtle but distinct shift from UX designer, which has existed for about 20 years.

I've been lucky enough to ride the wave with the times, always remembering my core purpose.

I was visiting a customer of ours in Denver this week. They're an HVAC contractor and we were camped out in one of their conference rooms where they teach their service technicians. On the walls, among posters of air conditioning diagrams were a couple of safety posters. At first glance they look like they're from the 1950s and ’60s, but upon closer inspection, they're from 2016! The only credit I can find on the internet is the copywriter, John Wrend.

Sadly, the original microsite where Grainger had these posters is gone, but I managed to track down the full set.

Such a gorgeous visual essay from Amelia Wattenberger. Beyond being wonderful to look at, the content is just as thought-provoking. Her experiment towards the middle of the piece is interesting. In our world of flat design and design systems, Amelia is truly innovating.

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: