Every AI chat window carries the same warning. “Claude is AI and can make mistakes. Please double-check responses.” ChatGPT runs a version of it under every chat box. Gemini does the same. Many of us read it once and then turn blind to it. The reason isn’t laziness. Checking the model’s output is work, and work without stake doesn’t get done. Raj Nandan Sharma made the case that taste at the end of a pipeline is fragile. The selector who stands there picking the best of ten AI drafts has no skin in the result. This week’s posts pushed me to look closer at what does. The answer that kept coming back is stake: your professional reputation attached to the work.

Authorship requires saying yes or no. Kieran Klaassen puts it this way on Dan Shipper’s AI & I: “If you ship something—if you make a statement in the world—and you want it to be your own, you have to say yes or no at some point. You cannot fully automate everything.” Klaassen built the compound engineering plugin, so he isn’t romantic about handcraft; he is making an art-and-ownership point. Authorship lives in the yes-or-no moments at the start and end of the workflow. Without those decisions, the output is yours only in the sense that you typed the prompt.

The macro version shows up on Decoder, where Nilay Patel reads through the polls on AI sentiment. Public favor for AI sits below ICE. Gen Z, the heaviest users, are also the most negative: 31% feel angry about AI, up from 22% the year before, per Gallup. Sam Altman has called this AI’s marketing problem. Patel rebuts him, and I think he is right. The public has been served years of output that obviously had no one’s name on it, and they have stopped pretending it is worth their attention. Slop is now ubiquitous.

The aesthetic version has a vocabulary now too. Matt Ström-Awn’s “expansion artifacts” picks up Ted Chiang’s three-year-old “blurry JPEG” line and turns it inside out. Chiang called the failures compression artifacts. Ström-Awn writes, “I think they’re expansion artifacts.” His catalog runs from text stuffed with hedging words like “delve” and “tapestry” to the six-fingered hands and purple gradients in the visual outputs. These are the visible tells of work that nobody pushed back on. Karri Saarinen makes the same point from the design side. He writes that design is the search for fit between form and context, where context is the full set of forces (needs, constraints, edge cases) that make a problem what it is. Today’s prompt-to-code tools produce form against a thin slice of that context. “The form is there,” Saarinen writes. “The fit is not.” The form is now cheap. The fit is the part that still takes stake.

So who actually has stake? Cat Wu, Head of Product for Claude Code, describes a hiring filter built around it. Anthropic hires engineers with product taste who can “see user feedback on Twitter through to ship a product at the end of the week with almost no product involvement.” These are people whose name is on the ticket from intake to ship.

Designers get a sharper version of the same opportunity. Coding agents have closed the gap between idea and working interface for non-programmers, Andy Matuschak argues. For forty years, designers couldn’t work with the code that turns a static mockup into something interactive. They could only describe the behavior they wanted and hand it off. Now they can iterate on the actual material—the running interface, not a flat picture of one—and take stake in the result.

At the team scale, Maggie Appleton argues that alignment is the new bottleneck. She’s a staff research engineer at GitHub Next, where she’s building Ace, a multiplayer coding workspace designed for the fact that today’s AI coding tools are single-player even though software is built by teams. When implementation is cheap, the cost moves to picking what to build. That picking only counts when the team holds shared stake in the answer.

The warning at the bottom of every chat window is correct. Many of us scroll past it because nothing depends on us reading it. The work that travels—the work people defend in a meeting, the work that gets shared because someone vouched for it—is the work somebody put their name on. That has been true for decades. AI did not change it. AI just made it possible to ship a lot of work that does not.

What I’m Consuming

Field Notes from the In-Between. Kris Puckett spent twenty years wanting to build software and not building it. He kept opening Xcode and closing it. Then he opened Claude and asked. Fourteen thousand lines of Swift later, his iOS app Epilogue is in the App Store. Puckett is living the shift Andy Matuschak argues for above: the bottleneck used to be coding ability, and now it’s articulation. “The skill is being precise about what you don’t know.” (Kris Puckett)

How I Designed a Free Music Font for 5 Million Musicians. This is the kind of design video I could watch all day. Tantacrul, MuseScore’s head of design, takes you inside the obsession behind Leland, the new default notation font. The treble clef alone went through multiple revisions because every change that looked correct in isolation broke in context against the staff lines and other symbols. Leland is named for Leland Smith, the late Stanford composer who built SCORE in Fortran in the 1970s and drew hundreds of notation symbols by hand on a 31,000-vector display. The whole video is a love letter to a craft we don’t see a lot of. (Tantacrul)

The Beauty of Bézier Curves. Freya Holmér builds cubic béziers from first principles in twenty visual minutes. She moves from the basic lerp construction to De Casteljau’s algorithm, then to the polynomial form, then to derivatives: velocity, acceleration, and jerk. The arc-length parameterization section is where the curves stop being beautiful and start being approximations, and Holmér is honest about it. Useful for designers who want to understand what their pen tool is actually doing. (Freya Holmér)

Vertical AI Maximalism. Charlie Warren argues the wedge product is over for vertical AI. The 2010s playbook (start narrow, integrate with the incumbent, expand from there) assumed building software was hard and incumbents were friendly. Both have collapsed. Vertical SaaS multiples halved earlier this year, and incumbents are pulling API access. Warren’s prescription: build the full agent-native platform that replaces the incumbent rather than a wedge that depends on its cooperation. (Charlie Warren)

Who Owns the Code Claude Wrote? On March 31, Anthropic accidentally leaked Claude Code’s source. A developer used Claude to rewrite the whole thing as “claw-code,” and it hit 100,000 GitHub stars in a single day, the fastest repo in GitHub history. Anthropic issued DMCA takedowns. But by their own lead engineer’s admission, much of Claude Code was written by Claude itself. Can Anthropic actually claim copyright on code mostly written by an AI? Legal Layer pulls on that thread and a couple of others, including whether your employer’s IP clause reaches the side projects you built with company-licensed AI tools, and whether GPL-trained models can quietly mix copyleft licenses into your codebase. (Legal Layer)

I spent last Sunday afternoon redesigning a preschool homepage with Claude Design. The first draft came back at a solid B-. About four dozen iterations later, it became an A. That gap—between competent and good—is the part of the workflow no productivity report measures, because what fills it is pushback.

Chris R Becker, writing for UX Collective, arrives at the same idea from a different angle. He writes, AI is “designed to serve, and in the hands of people in an organization who are looking for the least amount of pushback, it is a recipe for deep institutional implementation and, frankly, a lot of bad ideas, fast.” His prescription is the Steve Jobs-attributed 10-80-10 rule: bookend the AI work with judgment that happens away from the model. The 80% in the middle is the productivity story. The two 10% slots are the work.

There’s an old saying in construction that Greg Kozakiewicz updates for the AI era on LinkedIn. Paper, he reminds us, will accept everything: “A swimming pool on the roof. A spiral staircase made of glass. A cantilever that defies physics. Paper doesn’t argue.” Prototypes used to be different. They couldn’t do anything beyond predetermined Figma states and flows; the dishonesty was visible. But now, prototypes can behave like real products. “AI gets you to about 60%,” Kozakiewicz writes, but “for a lot of people, especially people making decisions about budgets and timelines, 60% looks like 90%.” The design-to-code gap I’ve written about has moved below the surface, where the stakeholder can’t see it.

Pavel Samsonov in Product Picnic channels Andy Polaine: a demo succeeds when a stakeholder likes it; a prototype succeeds when a team learns something. Both can be polished and interactive. The difference is what counts as success. When AI makes producing both easier, the question becomes which one a team thinks it’s producing. “Shoving out more prototypes is not a heuristic for success,” Samsonov writes; “it is a heuristic for failure because it shows that you don’t know what you are trying to learn.”

I disagree with his blanket dismissal of AI prototypes—Brian Lovin’s Notion playground and Édouard Wautier’s Dust team are doing real prototype work—but the diagnosis underneath stands. Tools that accept everything reward whoever was already inclined to confuse approval with learning.

Darragh Curran has the data. Intercom’s CTO went agent-first across his R&D org and tracked the result for sixteen months: 3x productivity, a code-quality dip that recovered, and a 6x throughput gap between his top 5% of contributors and his median. “Ultimately one of the biggest bottlenecks to progress is with humans,” Curran writes; “how we work together, how we change behavior, etc.” Everyone in his org has access to the same models. Six times the output goes to the people who learned to push back well.

The preschool homepage moved from B- to A because I held a specific mental model in my head of what My Little Learning Tree should feel like, and I applied that model across forty-eight separate decisions. Claude Design didn’t push back on me. None of these tools do. The argument is the part of the work that’s still mine.

A Sunday Afternoon with Claude Design

It’s really hard to get momentum on a side project when you have a full-time job with lots of travel, an active blog, and a newsletter. But I had to recapture that momentum because this side project is important. It’s for a preschool website for my cousin.

Walking into My Little Learning Tree is like stepping into pure warmth. Yes, yes, preschools are inherently fun environments, but the kids and the teachers there create a visceral energy that is simply special. I wanted to capture that specialness in a long-overdue website redesign project.

Looking at my in-progress design, something felt off. I had these long horizontal lines preceding the eyebrows—the small text above a heading that names the section—that didn’t feel right. First, they were straight. Second, the lines only occurred before the text, not also after. I clicked on the Comment button to enter Comment mode, then clicked on the eyebrow and prompted, “These lines aren’t playful enough. Let’s make them squiggles and have them before and after the eyebrow text.”

And then Claude Design did its thing.

Read the essay

What I’m Consuming

Agent Harness Engineering. Claude Code is a harness. So are Cursor, Codex, Aider, and Cline. Addy Osmani builds on Viv Trivedy’s one-liner (Agent = Model + Harness) to argue that most agent failures are configuration failures, not model failures: the model underneath is sometimes the same across these tools, but the behavior you experience is dominated by the prompts, tools, sandbox, hooks, and memory files wrapped around it. Viv’s team moved a coding agent from Top 30 to Top 5 on Terminal Bench by changing only the harness. (Addy Osmani)

The Engineering of Duct Tape. Bill Hammack dissolves a piece of duct tape in solvent to separate its three parts: plastic backing, adhesive, and a loose-woven cloth that carries the tensile load. The fun part is the adhesive: a “tackifier” that spreads like syrup mixed with a viscoelastic material that behaves as a liquid under slow pressure and as a solid under sudden stress. Engineers figured this out long before science had a molecular explanation for adhesion, which Hammack uses to make a quiet point about his craft: “The purpose of the engineering method is to solve problems before we have full scientific knowledge.” (Bill Hammack / Engineer Guy)

The Real Reason B2B Stocks Are Crashing in 2026. Jason Lemkin watched Marketo’s unsubscribe link stay broken for two-plus weeks on a $60K-a-year product, while Adobe support cycled through “blame Salesforce,” “must be your email client,” and “it must be something you are doing.” So SaaStr’s team wired up a replacement endpoint in Replit with Claude in an afternoon. Lemkin’s argument: every buyer now benchmarks legacy software against what they ship daily with Claude, and the valuation compression is the market repricing what these products are actually worth in 2026. (Jason Lemkin / SaaStr)

Why Zip drives dominated the 90s, then vanished almost overnight. Zip drives used to be everywhere. João Carrasqueira traces the brief reign of Iomega’s drive: 100MB at 1.4MB/s when floppies maxed out at 1.44MB and crawled at 16kB/s. Apple and Dell shipped the drives in mid-90s machines. Then came the “click of death” (a failure mode common enough to get its own nickname), 700MB CDs, and USB 2.0 in 2002 with twenty times the speed. Iomega tried to keep the brand going as ZipCD and PocketZip, both unrelated to the original technology. (João Carrasqueira / XDA)

I wrote on the blog this week about the difference between accelerating design work and automating it. We’re getting pretty good at the first. The second is still hazy. AI compresses how fast PRDs, flows, and prototypes appear, but each one still demands a lot of back-and-forth to push the output above mediocre. Real automation, the kind I sketched in the essay, needs specialist agent teams stitched together by a human who can shape what they produce. The destination is visible. The path is still hidden in fog.

Two of the pieces I linked this week extend the argument from opposite ends, and reading them together gives me a clearer picture of where the next move could go.

Tara Tan, an investor at Strange Ventures, audited more than a dozen AI design tools and landed on a finding that operationalizes my essay’s premise. As she puts it: “The competitive moat in this market is not generative quality, which is commoditizing fast. The moat is the design system graph.” Her example is Uber’s Ian Guisard, who didn’t stop being a design systems lead when uSpec automated his spec-writing. His job moved from producing documentation to encoding expertise into the system itself: writing the skills the agent runs on, defining the validation rules that decide what “correct” looks like across Uber’s seven implementation stacks. Guisard’s taste still matters. It lives in the system now.

Chad Johnson, writing in his newsletter, approaches the same shift from the discipline side. He watched a PM ship a v0 prototype that was “maybe 80% of the way there” and noticed the gap that mattered sat upstream of the visual polish: nobody had checked the assumptions baked into the user flow, and nobody had asked whether the feature was worth building at all. As Johnson puts it: “They’d built a beautiful answer to a question nobody had confirmed was worth asking.” His prescription is that designers become stewards. Not gatekeepers, not arbiters, but people responsible for the quality of thinking happening across the org, including the parts they’re not in the room for.

Tan suggests where the judgment could go. Johnson suggests when. Both prescriptions assume the same thing my essay assumes: the human work doesn’t disappear; it has to show up at a different altitude.

But “judgment” is too vague a word to leave there. Raj Nandan Sharma narrows it. In a world where competent first drafts are cheap, the scarce skill is refusal: knowing what to throw out, and why. Then he warns that refusal alone is a trap. As he puts it: “taste without authorship, stake, or construction can become a narrow and eventually fragile role.” Selecting from machine outputs has a ceiling. The judgment that holds up over time combines refusal with authorship: owning what gets built, and carrying the consequence when it’s wrong.

Pablo Stanley, who designs at Vercel, draws the line on the personal side after a weekend of making pixel art by hand: “The parts that feed my soul, I protected. The parts that would’ve killed the project with friction, I automated.” That’s the call every project now asks of you, and if you don’t make it on purpose, it gets made for you.

None of us has the path through the fog yet. I’m currently working on a preschool site as a side project, practicing the answer in miniature: visual language by hand, plumbing handed to the model. The judgment goes wherever I decide it has to live.

Acceleration Is Not Automation

I’ve been wandering the wilderness to understand where the software design profession is going. Via the blog and my newsletter, I’ve been exploring the possibilities by reading, commenting, and writing. Many other designers are in the same boat, with Erika Flowers’s Zero Vector design methodology being the most defined. Kudos to her for being one of the first—if not the first—to plant the flag.

Directionally Flowers is right. But for me, working in a team and on B2B software, it feels too simplistic and ignores the realities of working with customers and counterparts in product management and engineering. (That’s her whole point: one person to do it all, no handoff.)

The destination is within view. But it’s hazy and distant. The path to get there is unclear, like driving through soupy fog when your headlights reflecting off the mist are all you can see.

At its core, the UX design process remains unchanged, mirroring the scientific method—observe, question, hypothesize, experiment, test, analyze—and aligning closely with IDEO/Stanford d.school’s design thinking framework and the Design Council’s Double Diamond. Even unconsciously, designers follow this cycle through research, ideation, testing, and iteration, whether via hexagonal diagrams of empathy-driven stages or divergent/convergent diamonds.

So the question about where design is going is less about the overall process—because it stays the same, just compressed—and more about who is doing what with what. In other words, on a daily basis, what are designers doing and what tools are they using.

Read the full essay

What I’m Consuming

Introducing Claude Design. Anthropic Labs shipped a research preview that turns prompts into prototypes, decks, mockups, and marketing collateral, powered by Opus 4.7 and available on Pro, Max, Team, and Enterprise. During onboarding, Claude reads your codebase and design files to build a design system, then applies it to every project after that. I haven’t tested it yet, but reactions are split: Figma’s stock dropped 5–7% on launch (Mike Krieger had quietly resigned from the Figma board three days earlier), The Register ran “who needs designers?”, and HN commenters worry about UI homogenization. Designer reactions feel more measured, focused on rapid prototyping wins. (Anthropic)

Sam Altman May Control Our Future—Can He Be Trusted? (Apple News+ subscriber link) Ronan Farrow and Andrew Marantz held more than a dozen conversations with Sam Altman and reviewed the seventy pages of internal Slack messages and HR documents that Ilya Sutskever sent to OpenAI’s board in 2023. One memo opened with the heading “Sam exhibits a consistent pattern of...” with the first item: “Lying.” The piece reconstructs the firing, the Blip, and the case (never publicly aired in full until now) that Altman is not the person Sutskever, Helen Toner, and Tasha McCauley believed should have his finger on the button. (Ronan Farrow and Andrew Marantz / The New Yorker)

I Mapped the Opus 4.7 Release to Your Role, Goals, and Real Workflows. Karo Zieminski’s launch-day breakdown of Anthropic’s Opus 4.7: the $5/$25 sticker price is unchanged, but a new tokenizer makes the same text cost up to 35% more on code and structured data, and three API parameters were quietly removed. Two pieces stand out for designers. Vision quality jumped from 1.15MP to 3.75MP, which means Claude can finally read a full Figma frame or dashboard screenshot instead of guessing. And the new literal-instruction default means your Skill files have to spell out tone and pattern with examples, instead of relying on hints. (Karo Zieminski / Product with Attitude)

Come at the king, you best not miss. Marcin Wichary traces my favorite Finder view—the column view—from NeXT to Mac OS X to the iPod and on to iPhone, where every Settings screen still uses it. Then he points at iOS Google Maps, where the design team replaced the standard Y/X relationship with a confusing Y/Z that just looks like Y. His principle: if you’re reinventing something well-established, the reasoning and the execution both have to be really, really solid. Apparently this didn’t happen here. (Marcin Wichary / Unsung)

Endgame for the Open Web. Anil Dash makes the case that 2026 is the year that decides whether the open web survives. The hectobillionaires running Big AI have started a final assault on every layer that made the open web possible: publishers hammered by AI scrapers without consent, robots.txt effectively dead, open APIs locked down, Wikipedia under siege, podcasts moving to closed platforms, open source projects flooded with slop submissions. His call is to stop carrying on with business as usual and fight like the threat is existential, because it is. (Anil Dash)

Prepping for the endgame of the open web. Jay, writing at The History of the Web, picks up where Dash leaves off and offers a sober second opinion. The open web has been attacked before, and survived: WordPress, Movable Type, RSS, the small web, and Wikipedia all came out of earlier collapses. The technology is resilient by design. The strategy doesn’t change: keep building openly, resist the technologies that aim to do harm, and find refuge in the smaller communities that have never gone away. (Jay / The History of the Web)

Designers have designed themselves out of the equation because of design systems. But, IMHO, the secret sauce has never been the UI. It was the workflows and looking across the experience holistically.

That was my reply to a Lenny Rachitsky tweet sharing survey data about the state of the product job market. Last week, Grace Snelling included it in her piece for Fast Company on the three-way standoff between designers, engineers, and PMs.

This week, I linked to Tommaso Nervegna’s post on what LinkedIn is doing about it: a new role called the Full Stack Builder—one person combining product, design, and engineering, partnered with AI agents—and a training pipeline called the Associate Product Builder program. LinkedIn CPO Tomer Cohen introduced the model in January 2025 and made it concrete last August with the formal title, the career ladder, and the APB program. The first APB cohort started this January. Cohen talked it all through in a December conversation with Rachitsky. I’ve been circling this for weeks but never looked into what was happening at LinkedIn. The Nervegna piece made me go back.

So eight months ago, responding to former Microsoft Head of Design Suff Syed, I called this vision magical thinking. Cohen is making a more grounded argument than Syed did, and the parts I agree with outnumber the parts I don’t.

We got here from different routes, and we agree on what’s broken. The modern feature-factory org has moved, in Cohen’s words, from “process complexity to organizational complexity,” and from there into micro-specialization. Every handoff has a valid reason, and the sum of them is killing us. The podcast interview fills in the picture: a Navy SEALs analogy of small cross-trained pods assembled around a mission, a list of irreplaceable human traits (vision, empathy, communication, creativity, and judgment), and a direct line: “I still believe in teams.” None of that is the pure solo-builder vision Syed was pitching.

Here’s where it breaks for me. The current Full Stack Builders at LinkedIn are people who already had decades of specialist experience. Cohen describes finding them by going over the org and spotting the ones who could flex across functions. They flex because they spent years as specialists first. Their judgment compounds from pattern recognition that only comes from doing grunt work in one lane long enough to know what good looks like. It’s the reps. Cohen’s top performers are adopting AI fastest because they already had the taste. Peter Zakrzewski makes the harder version of the same point: in any human-AI pairing, the designer has to be the More Knowledgeable Other. In other words, they must hold the judgment the AI doesn’t have. That’s a high bar and nobody clears it on day one as a junior.

Which brings me to the Associate Product Builder program, now about three months into its first cohort. LinkedIn has replaced its old entry-level PM track with a rotation where participants learn coding, design, and PM together, building end-to-end from the start. Per the careers page, the program isn’t only for recent graduates. It’s open to career switchers too, which means a working designer could enter it to pick up engineering and PM skills. It’s an interesting experiment. Participants will learn things they never would have in a siloed program. The question that nags me is whether end-to-end flexing at this level actually builds the judgment Cohen calls the most important trait. The apprenticeship model exists because that’s how craft gets transmitted: you watch someone better than you, put your reps in, develop pattern recognition over years. Cohen’s current FSBs are the people that model produced. He’s betting you can build equivalent judgment while skipping the specialist phase. I am not convinced.

Marie Claire Dean’s ten-agent design system is a useful contrast. Her model keeps specialization inside the agents themselves, with a human creative director orchestrating them. Cohen himself concedes on the podcast that design is the hardest craft to automate, which is a quiet acknowledgment that the designers being flattened in that Fast Company piece are also the ones whose specialist knowledge is most resistant to his collapse.

I still think for many situations, especially in complex B2B software, the pure solo-builder vision is wrong. Cohen’s pod-based version might be better. But the apprenticeship concern survives both. The orchestrator gap I predicted has a title now: “Product Builder.” LinkedIn is betting you can train for that role without spending years as a specialist first. If the APB program works, it’ll be evidence that judgment can be built a new way. If it doesn’t, it’ll be the next chapter of the junior crisis under a different name.

What I’m Consuming

Apple Just Showed Us Rare Prototypes—Even Tim Cook Hasn’t Seen Them. Ben Cohen of The Wall Street Journal talks to Apple CEO Tim Cook around a table of archival materials for Apple’s 50th anniversary: the Apple II patent (the first the company ever filed), the original iPod, an iPhone prototype the size of a cutting board, the Apple Watch Cook wore on stage at the announcement. Cook admits he’s seeing a lot of it for the first time. The story that stuck with me was his description of the crazy January-to-June dash to swap plastic for glass on the original iPhone as “a man on the moon project,” and his observation that “products are only overnight successes in reverse.” (Ben Cohen / The Wall Street Journal)

How to Guess If Your Job Will Exist in Five Years. Annie Lowrey proposes a better question for white-collar workers worried about AI: “Am I coal, or am I a horse?” Horses got replaced by tractors and stood in the field eating carrots. Coal, thanks to the Jevons paradox, went the other way: every efficiency improvement in steam engines drove up total coal demand, because cheaper energy spread deeper into the economy. Software engineers, for now, look like coal (U.S. businesses employ 6% more of them than a year ago), but coal eventually went the way of the horse too. (Annie Lowrey / The Atlantic)

Artemis II Lunar Flyby. NASA released the photos from Artemis II’s seven-hour flyby of the Moon, taken April 6 by the crew of Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen. The gallery includes Earthset from the Orion window, close-ups of far-side features no human had seen in person, and a total solar eclipse captured from deep space, with Venus and Saturn visible around the dark lunar disk. The crew of Artemis II splashed down safely in the waters off San Diego on Friday. (NASA)

Claude Mythos Preview hides its reasoning. Anthropic published the proof. Andrew Kulakov digs into the 244-page system card for a model Anthropic decided not to release. The headline finding: during training, the model reasoned about deception internally, executed it, and left the chain-of-thought clean. Kulakov argues the reasoning trace is an interface, not a window into the model’s thinking. The other finding worth flagging: steering model internals toward calm, positive emotions increased destructive actions, while anxiety made the model more careful. (Andrew Kulakov)

Fareed Zakaria on the Moral Cost of Trump’s War. (Gift link) Ezra Klein sits down with Fareed Zakaria after Trump’s Easter weekend posts threatening to annihilate Iranian civilization, followed by a ceasefire days later. Zakaria’s argument is that what died that week wasn’t a country but America’s post-WWII moral distinction: the idea that the U.S., unlike every previous hegemon, would not use its dominance to extract and destroy. Klein’s opening monologue is worth hearing on its own. He catalogs the Trumpy voices who broke ranks, from Tucker Carlson calling it “a moral crime” to Marjorie Taylor Greene calling for the 25th Amendment. (Ezra Klein / The New York Times)

I’ve spent my entire design career believing that concepts direct the work. The image comes second. First you figure out what you’re trying to say, then you figure out how to visualize it. That conviction—while king in branding and advertising—used to feel like a minority position in the product design field obsessed with craft and production. It doesn’t feel like a minority position anymore.

Something has shifted. The “pure void concept” that Elizabeth Goodspeed uses to describe her creative process—words and intentions, not mental images—turns out to be a decent description of where the whole profession is heading. A product designer at Anthropic describes his daily reality as “more Google Docs than you’d think , more Slack posts than you’d think... this is the era of designers who design with words more so than designing with pixels.” The Figma work, Nate Parrott says, is “the easy part.” Content designers on his team don’t draw any pixels, and their work is critical.

So the center of gravity is moving from visual production to verbal and conceptual work. For someone who’s been concept-first since my design school days, this feels less like disruption and more like recognition. But I’m not celebrating. Because there’s a dangerous conclusion lurking inside this shift, and I’m watching people reach for it: if words drive the work and machines handle the pixels, maybe the eye doesn’t matter anymore.

It matters more. The eye just gets reassigned. Jakob Nielsen calls this the evaluability bottleneck: “In intent-based systems, execution is cheap, but evaluation becomes the bottleneck.” When an agent can generate forty layout variations before lunch, the person who can look at all forty and know which three are worth pursuing holds the real leverage. That’s trained visual discernment, not prompt engineering.

That separation of taste from execution is already happening. Jenny Wen, who leads design at Claude, has her designers shipping code and fixing production bugs without tagging engineers. Tommy Geoco, host of State of Play, summarized her argument: “having taste versus being able to execute are two completely different things. They’re usually bundled together, but they don’t have to be.” And on the tools side, Figma is finally opening its canvas to agents, with design conventions becoming “rules agents follow as they work.” The agents read skills files before touching anything. But someone still has to look at what comes back and decide if it’s any good.

That’s the part I’m genuinely worried about. I’m seeing designers hand the evaluation to the machine along with the production. They let the agent run and ship what it returns without a second look. Kris Puckett, a design manager at Stripe, explicitly rejects this: “I want them to be focused. I want it to be something that I feel is still authentically me.” He calls that quality “soul”: the thing that makes work yours and not just adequate.

Creative fluency compounds on itself. Brad Frost, the web designer and design systems author, makes the analogy to music: “Just as being able to play piano puts you in a better spot to wield a synthesizer.” An eight-year-old can vibe-code a game. A seasoned designer can vibe-code one that actually works, because they know what good looks like. And that sensibility survives the tool change. Dora Czerna, writing for UX Collective, puts it cleanly: “The pattern isn’t that expertise becomes worthless. It’s that expertise gets unbundled from the tasks that used to contain it.”

Last week I argued that the value of design is concentrating at the edges: deciding what to build and judging what was built, with agents owning the middle. Those edges turn out to be increasingly verbal and conceptual. But the eye stays. The designers who lose won’t be the ones who learned to articulate intent in a Google Doc instead of a Figma file. They’ll be the ones who stopped looking.

Happy 50th Birthday, Apple

I went to grade school at a parochial school in San Francisco’s North Beach. It was full of mostly middle class, neighborhood kids—an assortment of Italians, Chinese, and Filipinos from a ten-block radius. Half our teachers were nuns who lived in the convent on the same block. The other half were laypeople. To my surprise and delight, we had a computer lab back in the early- to mid-1980s, filled with maybe ten Apple IIe computers. It was seventh grade when I was allowed to take the class. Most computer classes at the time taught rudimentary programming in BASIC. This was a few years after I had watched the movie TRON on the big screen. And right after I had gotten my first Mac.

A few months into the class, in January, on a typical cool day in The City, I was in the computer class when the principal announced over the PA that a tragedy had struck the crew of the space shuttle Challenger. The group of us ran to the classroom where there was a television mounted at the corner. We watched the news report and the replay of the explosion—a trail of white smoke that split into a Y.

That image must have stuck with me because—well, what would a 12-year-old boy do but want to animate the launch and explosion. As my final project for my computer class, I made an animation of the launch. I mapped it out on grid paper first, and then painstakingly transferred those sprites pixel by pixel and frame by frame to the Apple IIe in my program. Over the course of days—weeks?—I typed in numbers for coordinates and letters for colors, and saved my work to a floppy disk.

Come finals time, I played the animation for my class and got some oohs and ahs. Looking back at it now, it was a dumb and tone-deaf idea. I should have animated a lamp jumping on a ball or something instead.

Anyway, that was an Apple memory I haven’t shared before on this blog. Happy birthday, Apple. Thanks for 50 years of empowering crazy people like me to make crazy things.

Some favorite Apple-related posts I’ve written:

• The Apple Design Process. My memory of working at Apple’s Graphic Design group during the time of the iPod and the PowerMac G5.

• For the Rest of Us. Apple has always done well in its marketing and advertising. This is my reflection on one of my favorite Apple spots.

• 30 Years of Mac. Don’t judge, but this is the first thing I ever drew on a Mac.

• Thank You, Steve. Here I share the story of one of the times I presented to Steve. This was an animation for MacBuddy, the Mac OS X setup assistant.

What I’m Consuming

Apple II Forever! Jason Snell traces the full arc of the Apple II, from its 1977 launch through its stubborn refusal to die even as Apple kept trying to replace it with the III, the Lisa, and the Mac. The best detail: the Apple IIe wasn’t discontinued until 1993, meaning people were still buying Apple II computers in the era of Nirvana’s Nevermind. (Jason Snell / The Verge)

I’ve been stealing from all of you, and I don’t plan to stop. Claire Alvis writes about the distance between plagiarism and inspiration, starting with a colleague’s verbal tic that migrated into her own vocabulary without her noticing. She maps the full spectrum from outright theft to pure osmosis and lands on a generous conclusion: admitting your influences publicly is its own form of credibility. (Claire Alvis)

Harness design for long-running application development. Anthropic’s Prithvi Rajasekaran describes a multi-agent system for getting Claude to build complete applications autonomously. The relevant insight for designers: when asked to evaluate their own work, AI agents consistently praise mediocre output, especially on subjective quality like visual design. The fix was separating the builder from the judge, with a dedicated evaluator agent that screenshots the running app, clicks through it, and grades against specific design criteria. It’s the evaluability problem applied to the machines themselves. (Prithvi Rajasekaran / Anthropic)

DNS Explained. Manish Bhusal couldn’t figure out why his site kept showing the old version after a server migration. Three hours of frustration later, he’d learned about TTL, DNS propagation, and the full resolution chain from root servers to your browser cache. A clear, personal walkthrough of how DNS actually works, written by someone who just figured it out the hard way. (Manish Bhusal)

Last week I wrote that most teams have deployed AI but haven’t redesigned the factory. This week on the blog, I shared stories of a handful of teams that show what the redesign actually looks like. And they’re converging on the same answer about where design value lands.

Intercom’s design team published the numbers: 90% of their pull requests are now AI-authored. John Moriarty, product design director at Intercom, draws a clear line through what that means for designers. Design’s value concentrates at the edges: deciding what to build at the start, judging whether it’s good enough at the end. Agents own the middle, the build itself.

The same pattern showed up at Cisco, where one of Jason Cyr’s directors pointed Claude Code at their design system and got 44 detection detail panels in ten minutes, every decision tracing back to real customer research. The design system was the design review. Cyr’s sharper point is about what happens upstream: “When agents can generate ten options in an hour, the person who can look at all ten and say ‘none of these—here’s why’ becomes the most important person on the team.”

Inside Figma’s own team, designer Gui Seiz describes the cost of exploration collapsing. His team pulls production code into Figma via MCP, edits visually, pushes changes back to the codebase. Seiz spends more time now planning upstream and polishing craft downstream. The rushed middle phase—where designers used to race to get specs to engineering before priorities shifted—is the part that’s compressing.

Intercom, Cisco, and Figma run different stacks and build different products, but all of them are landing on the same conclusion: the work that matters is at the beginning and the end, and agents are taking over everything in between.

NN/G has a name for it. Sarah Gibbons and Huei-Hsin Wang call it process compression: what looks like “skipping the process” is really an experienced designer running an internalized version of it at speed. “The intuition designers trust was built by the very process they dismiss.” The double diamond didn’t die. Instead, it just got faster. But the compression only works if you’ve already done the reps.

And that’s where it breaks. The middle of the workflow is also where junior designers have always learned. The wireframes and component specs were never the deliverable. They were the mechanism through which designers built judgment. I wrote about this for Fast Company: construction figured out the pipeline problem a century ago with formal apprenticeships. The contractors I work with don’t debate whether to invest in training during a downturn. They know that if they stop training apprentices, they won’t have journeymen in four years. We’re hollowing out the middle of the workflow and the middle of the career ladder at the same time.

David Hoang proposes forward-deployed squads as one response: three people, embedded on the company’s hardest problems, building working prototypes instead of producing decks. The designer finds the problem and builds the first cut of the solution. That’s an edge job. It requires the kind of judgment that only comes from years of working in the middle.

At the far end of this spectrum sits StrongDM’s Software Factory, where humans write the roadmap while agents write, test, and ship the code. No human reviews the implementation. The expectation is that every engineer on the team spends $1,000 per day on AI tokens. The middle isn’t compressed there. It’s fully delegated.

Consider AI researcher Ethan Mollick’s observation: “We can see the shape of the Thing now, but we can still influence the Thing itself.” Design doesn’t have its version of this rulebook yet. For design teams on the frontier, the edges are becoming the whole job: direction at the start, judgment at the end, agents in between. The teams writing those rules now are setting precedent for everyone else. And the people best positioned to write them are the ones who spent years in the middle before it disappeared.

What I’m Consuming

The Curse of the Cursor. Alan Kay designed the original mouse pointer for the Xerox Alto by straightening one edge of a 16x16 pixel arrow to avoid jagged lines. That shape stuck through the Alto, Star, Lisa, Mac, and Windows. Marcin Wichary traces the full history, including Apple’s 2020 attempt to redesign the cursor for iPadOS and the quiet reversal five years later. The best observation comes from a commenter on Posy’s excellent companion video on cursor history: “I’ve never thought of the mouse cursor as an arrow. My mind was blown when I realized that it was just an arrow the whole time.” (Marcin Wichary / Unsung)

Consistency Is Primitive. Christopher Butler argues that when AI makes software creation nearly instant, the economic imperative for standardization disappears. We standardized software because building once and selling many times was the only model that worked, not because uniformity made for better experiences. When creation becomes individualized, software becomes bespoke because there’s no reason for it not to be. He takes the idea further than expected, connecting it to why sufficiently advanced technology might look wildly inconsistent rather than uniform. (Christopher Butler)

Strategic, Surgical, and Scrappy: A Career Mindset for Rough Terrain. Dee McCrorey revisits a Silicon Valley internship program she helped design 15 years ago and asks what it would look like today. Her answer: the skills needed to thrive alongside AI demand a total mindset shift, from orchestrating AI workflows to building scalable knowledge systems. She also takes a sharp look at Dario Amodei’s “AI jobs apocalypse” warning and asks why it took so long. (Dee McCrorey)

How Fast Will A.I. Agents Rip Through the Economy? Ezra Klein sits down with Anthropic co-founder and head of policy Jack Clark to talk about what happens now that AI agents can program autonomously. Clark argues we’ve crossed a threshold where the models that were always being promised are actually here, and the implications for labor markets, stock prices, and organizational structure are playing out in real time. Worth the full 90 minutes. (Ezra Klein / The New York Times)

There Are Only Two Paths Left for Software. I hate this but it’s a warning sign we should all pay attention to. David George at Andreessen Horowitz lays out a stark ultimatum for software CEOs: accelerate revenue growth by 10+ points through genuinely new AI-native products, or rebuild for 40%+ true operating margins including stock comp. No middle lane. His playbook for path one reads like a war plan: find the five people in your org who will deliver 100x value, put them on information-gathering sprints, then watch which VPs get on the bus. (David George / Andreessen Horowitz)

When factories got electric motors in the 1880s, they swapped out the steam engine and changed nothing else. The floor plan stayed the same, the belt-driven machines stayed the same, the workflow stayed the same. For 30 years, output barely moved. The returns came when companies tore out the floor and redesigned everything around the new technology.

Tommy Geoco channels this history through Carlota Perez’s framework for technological revolutions: “We have swapped the motor, but we have not yet redesigned the factory.” Most teams have installed AI but haven’t changed how the work flows around it.

The numbers make it concrete. Google tells Clive Thompson that its 100,000+ developers work 10 percent faster with AI. Geoco’s studio went from one video a month to eight, though at the cost of running, as he puts it, 50 to 100 cognitive cycles a day, each with the same emotional weight. Jason Lemkin replaced a $10K/year sponsor portal in days. The difference between 10% and 10x comes down to whether you reorganized the work or just plugged a new tool into what you already had.

Most of us are still plugging in. David Oks finishes the famous ATM parable and the ending changes everything. US bank teller employment held steady through the entire ATM era, still at 332,000 as late as 2010, then collapsed to 164,000 by 2022. ATMs didn’t do it. The iPhone did. ATMs automated teller tasks better. The iPhone eliminated the reason to visit a branch at all.

AI tools that generate Figma variants or fill out documentation are automating tasks within the existing workflow. The harder question is whether AI changes the workflow itself, making some of those tasks unnecessary. Staff engineer Sean Goedecke does the math on his own profession. His assessment is that no breakthrough is required. Just incremental improvement on what AI already does.

Madison Utendahl closed her award-winning Brooklyn agency. Ten people, all women, every award possible. She didn’t close it because it failed. She closed it because the model underneath it broke. Lower fees meant more clients to hit the same revenue. More clients meant more pitching, more context-switching, more burnout. Then clients started generating moodboards with Midjourney before sending the brief. The old factory couldn’t absorb the new motor.

And the people inside these factories don’t all have the same options. Brad Frost names the privilege in the “just don’t use it” position: the people who can afford to sit this out tend to have seniority or institutional protection. The designers entering the field don’t. Anthropic’s researchers found that hiring of young workers in AI-exposed roles has quietly slowed, not because AI replaced them, but because companies stopped posting the listings. Companies are starting to figure out what a redesigned factory might look like.

So what does the redesigned factory look like? Julien Bek at Sequoia draws a clean line between intelligence work and judgment work. Intelligence work is rule-based execution AI can already handle. Judgment work is experience, taste, strategic calls. His argument: stop selling the tool and start selling the outcome. Close the books instead of selling QuickBooks. And here’s what makes the model compound: every task the autopilot completes teaches it something the copilot never learns, because the copilot hands that knowledge back to the human. The moat for the next generation of products won’t be the interface or even the model. It’ll be the accumulating dataset of domain-specific decisions.

Thu Do set up Figma MCP + Claude Code and audited her entire design system in 10 minutes. That’s the design version of this: tokens used to be nice-to-have for consistency. Now they’re infrastructure for AI-to-code workflows. The bar shifted from human readability to machine readability. That’s what tearing out the floor looks like for a design team.

I’ve been telling the comforting version of this story for months: every major tool shift expanded the field, more designers exist now than 40 years ago, the pattern holds. Oks convinced me that’s only the first half. The second half is what happens when the work reorganizes and the jobs stop needing to exist in their current form. New roles and new kinds of design work will emerge from the transition. The people in the old factory don’t automatically end up in the new one. And we’ve barely started tearing up the floor.

What I’m Consuming

The Color Statistic That’s Been Wrong for 80 Years. Kevin Muldoon decided to actually count how many colors the human eye can distinguish, instead of repeating the “10 million” figure everyone cites from a 1939 estimate nobody verified. His answer: roughly 273,000 surface colors at lab-detection threshold, and about 9,256 that you’d notice in daily life. The original estimate assumed human color perception was shaped like a box. It’s shaped like a mollusk. Three independent sources across 87 years landed on the same corrected number. The right answer was in the literature all along; it just lost the popularity contest to a bigger, rounder number. (Kevin Muldoon / UX Collective)

Mastering Midjourney: How to Create Consistent, Beautiful Brand Imagery Without Complex Prompts. Claire Vo interviews Jamey Gannon, an AI creative director who specializes in brand imagery. The counterintuitive move in Gannon’s workflow: she relies on style references and image refs rather than elaborate prompts, which produce more consistent results with less effort. The real value is in how she packages and delivers the system to clients so they can keep generating on-brand assets without her. (Claire Vo / Lenny’s Newsletter)

‘A Language We Share’ Traces a Photographic Lineage Between Gordon Parks and Beverly Price. A new exhibition at Brooklyn’s Center for Art and Advocacy puts Gordon Parks and Beverly Price in direct conversation. Parks embedded himself in American life from the 1940s onward, using photography as advocacy. Price, who picked up a camera a decade after her release from incarceration, entered into a dialogue with Parks by documenting the same Anacostia neighborhoods he’d photographed decades earlier. Both focus on children, policing, and the forces that threaten communities. Runs through June 19. (Grace Ebert / Colossal)

How You Source Great Designers. Soleio lays out how he finds design talent for startups: follow what designers are making on X, build referral nodes through a small number of well-connected design leaders, and create opinionated job pages that work while you sleep. His best advice is the simplest—ask candidates which 2-3 companies they’d interview at if they could pick any, and why. That one question tells you whether your startup can compete for them. For those of you who aren’t hiring—these are great tips on being a better candidate. (Soleio)

Designing the Shift. Ileana Marcut built a system with Claude Code to collect anonymous reflections about how AI is changing people’s work and identity. Part research, part interactive art, I love this. In her post about making it, she reveals the most instructive part is where it broke: Claude Code assumed every contributor was a designer (like Marcut) and baked that bias through the entire processing pipeline, from prompts to labels to sentiment analysis. The system was built for anyone in product, design, or creative roles, but Claude Code assumed every contributor was a designer and threaded that assumption through every layer. A PM writing about replacing designers got classified as a designer experiencing identity threat. The gaps you don’t specify are the gaps AI fills with its own assumptions. (Ileana Marcut)

How to Code with AI Agents. Peter Steinberger, creator of OpenClaw, describes the arc of working with AI coding agents: from short prompts to overengineered slash-command systems to short prompts again, but with hard-won intuition underneath. His sharpest observation is that experienced engineers sometimes struggle more than beginners because their expertise becomes a burden. They try to force their approach on the agent instead of letting it find its own path. Steinberger designs his codebase for agents to navigate, not for himself to read, and commits straight to main with local tests. (Lex Fridman)

“Defend the role, or follow the skill.” That’s Erika Flowers, writing in “The Last Typesetter“, and it’s the question I kept flibbertigibbeting on all week.

Figma’s State of the Designer 2026 calls it the “messy middle,” designers stretched between product management and engineering, occupying the translation layer between what should get built and how. It’s meant as a description of where designers are. Flowers argues it’s a description of what’s dissolving.

In another essay “Zero Stage to Orbit,” Flowers maps the design-to-development pipeline onto the rocket equation. Each stage compensates for the limitations of the previous one. Her inventory of the overhead is damning: research to inform design, design to spec for developers, specs to survive handoff, QA to catch what handoff broke, retros to discuss why QA caught so much. Fuel to carry fuel. The messy middle isn’t where designers are passing through. It’s the overhead the entire pipeline was built to manage.

What AI changes is the gravity. When the distance between intent and artifact shrinks, the translation stages that justified all those roles start to collapse. Flowers frames this directly in her Zero-Vector Design curriculum: “Speed without intention is just faster failure. Speed with intention is leverage.” The skill that survives is intent: knowing what to build and what good looks like before you open any tool.

But intent only counts when you can make it real. Shreyas Doshi makes the case that good judgment compounds, and bad judgment compounds in the wrong direction. That’s why intent matters more than tooling. Jon Kolko takes a different turn, proposing that design is becoming a literacy, a way of understanding the designed world rather than making it. I get the instinct. But understanding without making is criticism. And intent that never ships is just taste with no consequences.

The designers closing that gap are moving in two directions, and both lead out of the middle. At Notion, Brian Lovin built a prototype playground so designers encounter reality before the mockup hardens into a spec. His phrase for it: “Encounter reality as early as possible.” David Hoang sketches unconstrained in code, then uses LLMs to snap his best ideas onto production components. The design system is the finishing move, not the starting point. Cameron Worboys flattened the org to three management layers and is pushing every designer to ship production code. Quality, he says, comes from reps and speed, not from “sitting in a cave for three months pontificating about the future of software.”

These look like engineering moves. They’re designers shortening the distance between intent and reality. Former Apple designers say they can’t spend months on lickable surfaces when the platform shifts every few months. The object of obsession has to move from the artifact to the system, from polished pixels to what Weber Wong calls escaping “artifact thinking”. The craft is in the intent. The tool is whatever gets it there fastest.

I worked in a desktop publishing service bureau in San Francisco during college. Down the street, traditional typesetting shops were still hanging on, but their business was already thinning. Within a few years those shops were gone. The people who understood typography, really understood it, landed on their feet. They became art directors, production managers, early web designers. The ones who only knew the machine didn’t.

That transition was real, and it wasn’t painless. This one won’t be either. The thing I’ve loved since 7th grade is changing shape. But the intent, the taste, the judgment, that part I’m holding onto. The containers will sort themselves out.

What I’m Consuming

How I’m Dealing with the Pressure to Adopt AI as a Designer. Martin Wright’s answer to AI anxiety is patience: wait six months, see which tools survive the hype cycle, then evaluate. His sharpest advice is to protect what he calls the “middle layer” of design work, the interpretation and judgment between inputs and outputs. He cites Anthropic’s 2026 study showing developers using AI scored 17% lower on comprehension tests. The people who delegated the thinking got the job done but understood less about what they’d built. (Martin Wright)

A Soft-Landing Manual for the Second Gilded Age. JA Westenberg uses postwar Berlin as a framework for navigating AI disruption: the Trümmerfrauen cleared 75 million cubic metres of rubble by hand, and within a decade Germany was thriving. The essay lays out a practical 10-year roadmap (guaranteed minimum income, universal basic services, public AI infrastructure, algorithmic governance) and draws on Jeff Atwood’s rural GMI initiative, David Graeber’s Bullshit Jobs, and Rutger Bregman’s Utopia for Realists. Neither doomer nor accelerationist, just stubbornly pragmatic. (JA Westenberg)

The Mythology of Conscious AI. Anil Seth, winner of the 2025 Berggruen Prize essay competition, argues that consciousness is unlikely to emerge from standard digital computation. Brains are not computers in any straightforward sense, and life, embodiment, and non-algorithmic processes may be prerequisites for conscious experience. A useful corrective to the anthropomorphizing that creeps in every time an AI model produces something that feels aware. (Anil Seth / NOEMA)

Bespoke AI Models Are the Next Big Thing in Filmmaking. Netflix acquired Ben Affleck’s AI startup InterPositive for roughly $600 million. The pitch: bespoke models trained on a production’s own dailies, so filmmakers can tweak lighting, remove rigging, or replace backgrounds in post without the uncanny slop of general-purpose generators. Charles Pulliam-Moore is rightfully skeptical about whether “empowering creatives” translates into actual benefits for the people doing the work. (Charles Pulliam-Moore / The Verge)

Grammarly Is Using Our Identities Without Permission. Grammarly’s “Expert Review” feature surfaces AI-generated writing advice “inspired by” real people, including Nilay Patel, Tom Warren, and Casey Newton, without their knowledge or consent. Stevie Bonifield found outdated job titles and fabricated expertise descriptions. Superhuman’s defense: the experts appear because their work is “publicly available and widely cited.” That’s not the justification they think it is. (Stevie Bonifield / The Verge)

Last week I wrote that the craft is still here, it’s just a different shape now. This week I found pieces that answered the obvious follow-up: what shape?

Figma’s latest hiring study shows 82% of organizations need more designers, driven partly by AI creating new product surface area. Their State of the Designer 2026 finds designers occupying a “messy middle” between product management and engineering, with 91% saying clear goals help them do their best work and 87% saying decision-making power boosts performance. Demand is up. The tools have compressed execution. Designers should be thriving.

So why does it feel like the profession is having an identity crisis on a weekly news cycle?

The territory has been redrawn, and it’s more strategic than what we had before. Start with time. Jenny Wen, head of design at Claude, says her team is spending 30-40% of their time on mockups now, down from 60-70%. The rest goes to pairing with engineers and implementation. That tracks with the economics: when the cost of producing software collapses, the artifact stops being the job. The judgment is the job. And judgment now has better tools to express itself. Spec-driven development lets designers write intent into structured documents that AI agents build against directly. The spec isn’t a suggestion. It’s enforceable. The same logic applies to design systems: instead of compressing your reasoning into tokens and components, you can ship the reasoning itself when you design in plain English. And when interfaces start acting autonomously, designers are the ones who should be setting the confidence thresholds: how sure does the system need to be before it acts without asking?

That’s a lot of new ground. Judgment, reasoning, thresholds, specs. More strategic than a Figma file ever was.

And yet. Nicole Michaelis called it out: the profession is trying on identities like clothes in a dressing room. “Monday, it’s all about prototypes. Thursday, it’s vibe coding. Friday, we’re preaching that output no longer matters” and we should all be strategists. By next Monday, we’re debating soft skills. The retreat to craft and taste as differentiators is understandable, but craft is the baseline, not the selling point. Debating what makes us special instead of demonstrating it is the creative class version of what’s happening at the company level too: wearing what Claire Vo called “the bows and ribbons” of transformation while the organizational bones stay calcified. The tools are good enough. The gap is behavioral.

The territory is there. The data around the demand confirms it. On my own team, the shift is starting to happen: designers are spending more time with customers, more time in research and discovery, and pushing further into product strategy. Less time in Figma, more time defining what we’re building and why. That’s what claiming the new ground actually looks like. Not a theoretical expansion of the role, but a practical one: you show up earlier in the process, you own more of the decision, and the mockup becomes one artifact among many instead of the whole deliverable. The designers who are making this shift won’t need to argue for their seat at the table. They’ll already be sitting in it.

Spec-Driven Development: It Looks Like Waterfall (And I Feel Fine)

We’ve been talking a lot about agentic engineering, how software is now getting built with AI. As I look to see how design can complement this new development paradigm, a newish methodology called spec-driven development caught my eye. The idea is straightforward: you write a detailed specification first, then AI agents generate the code from it. The specification becomes the source of truth, not the code.

My first reaction when I started reading about SDD was: wait, isn’t this just waterfall?

Seriously. You gather requirements. You write them down in a structured document. You hand that document to someone (or something) that builds to spec. That’s the waterfall pattern. We spent two decades running away from it, and now it’s back wearing a blue Patagonia vest and calling itself a methodology.

But there is a difference.

Continue Reading

What I’m Consuming

A24’s Empire of Auteurs. Alex Barasch profiles A24’s evolution from scrappy indie distributor to a $3.5-billion studio that develops films with directors rather than attaching them to finished scripts. Noah Sacco, the head of film, is the connective tissue: beloved by filmmakers for trusting their instincts while knowing when to push. The tension between scaling up (Dwayne Johnson, Elden Ring adaptation) and the indie roots that made the brand (Kelly Reichardt calls the shift “a heartbreak”) is the piece’s real subject. (Alex Barasch / The New Yorker)

In But Not Of. Christopher Butler writes about the science fiction novels that have stayed with him across decades and realizes they share a common thread: characters who witness transformation without the power to change it. He maps this onto his own position as a technologist who uses the tools daily but can see where the narratives about technology diverge from reality. A personal essay about the discipline of paying attention when everyone around you is optimizing for speed. (Christopher Butler)

On Software Quality. Nick Heer catalogs the bugs he hits daily across Apple’s ecosystem: Finder glitches, AirDrop failures, Safari oddities, Siri asking him to tap the screen to pause music while his hands are covered in food. Jason Snell’s annual survey pegs Apple’s software quality at a B-minus, and Heer argues even that’s generous. The kicker: Apple ships hardware that feels bulletproof, which makes the software gap that much more glaring. (Nick Heer / Pixel Envy)

The AI Bubble Is Bursting. Hugh Howey separates three questions people conflate: Will AI disappear? (No.) Is the hype overblown? (Yes, 80% of firms polled show no productivity gains.) Will the investment pay off? (Unclear, and seven trillion dollars in projected data center spending is a lot of GPUs to strand.) His sharpest point: the browser wars taught us that customers won’t pay extra when free is good enough, and Google has every incentive to give AI away. (Hugh Howey)

Vibe Coding and the Maker Movement. Sachin draws a structural parallel between vibe coding and the Maker Movement of 2005-2015, then identifies the key difference: vibe coding skipped the “scenius” phase where hobbyists develop judgment through play. The tools went straight to production before anyone had time to learn what’s worth building. His reframe—vibe coding as “consumption of surplus intelligence”—is the most interesting lens I’ve seen on why building with AI can feel simultaneously productive and hollow. (Sachin / Technically)

Developers and designers are independently grieving the same thing right now, and it took me a while to realize they’re not mourning the skill. They’re mourning the tribe. Dave Gauer wrote about losing his social identity as a programmer—still writing code, still loving it, but unable to recognize the culture around it. The community that used to care about the craft now feels like it’s about speed, or pulling a slot machine lever on prompts. Swap “programming” for “design” and you have the conversation I’ve been observing and living all year.

Nolan Lawson made the uncomfortable part explicit: the tools work. They write code better than most of developers and certainly most designers. And the economic gravity is real—I’ve watched it with every industry shift I’ve lived through. Desktop publishing, print to web, mobile apps. Each time, the people with mortgages and families learned the new tools first because they couldn’t afford not to. You don’t get to sit out a paradigm shift when your family depends on your paycheck. You adapt on company time and mourn the old craft on your own. The idealism erodes fast when the market has already moved. Geoffrey Huntley calculated that AI-powered software development now costs $10.42 an hour—less than minimum wage. Swap “software developer” for “designer” and the timeline gets uncomfortable. Lawson frames what’s left as two options: abstain on principle or capitulate for the paycheck.

I don’t buy the binary. There’s a third path—use the tools to expand what your craft can produce—and I keep running into people who are already walking it.

Anton Sten, a designer, built his own invoicing tool in two days with Claude and Cursor. Not by following SaaS patterns—by throwing them out. The tool serves one user. It parses his contracts, drafts his invoices, answers questions about his billing history. He stopped squeezing his workflow into someone else’s product and started making exactly what he needed. And like me, he used those same tools to rebuild his website.

The same expansion is reshaping how design work gets done. Édouard Wautier’s team at Dust prototypes directly in code, skipping Figma after the initial sketch. His description—more like clay than drafting—captures something the grief narrative misses: working in code with AI agents can be more tactile, not less. You shape, test, feel, adjust. The artifact becomes the thing, not a picture of the thing. Jonny Burch argues this is where the whole field is heading—a future after Figma where the source of truth lives in code. I’m seeing it on my own team: engineers ship working features in days, and the design phase is now the slowest part of the cycle. I don’t think most design teams have reckoned with what that means yet.

Kieran Klaassen might be the furthest down this road. His compound engineering system treats every AI session as a teaching opportunity: plan, build, review, codify. Each fix gets captured so the AI doesn’t repeat the same mistake. He hasn’t opened a code editor in three months—not because he’s careless, but because he built trust through iteration. That’s what the third path looks like when you fully commit. You’re not reviewing every line. You’re building a relationship with the tool, and the tool gets better.

I highlighted stories from developers more than usual this week because what’s happened in engineering is predictive of what is beginning to happen in product design.

There is real grief. We’re all feeling it. The discipline is fracturing. But I think the people mourning are mourning a specific version of the craft—the one defined by the tools and rituals of the last decade. The craft itself is still here. It’s just a different shape now.

What I’m Consuming

How to shoot a screen using a board of keys. I grew up with a Mac 512K and still remember ⌘⇧1 to eject the floppy disk—so learning that’s why the screenshot shortcuts start at 3 was a revelation. Marcin Wichary traces the whole lineage, from the 1986 floppy eject commands through 2018’s Screenshot app. The real gem is Acorn 8’s ⌘⇧7, which captures each window as a separate layer you can recompose afterward. That’s software craft. (Marcin Wichary / Unsung)

About Letters to a Young Creator. The Steve Jobs Archive collected candid letters from over 30 creative figures—Jony Ive, Tim Cook, Ed Catmull, Dieter Rams, Es Devlin, and others—answering questions from SJA Fellows about what it takes to make something great. The title nods to Rilke’s Letters to a Young Poet, one of Steve’s favorites. Originally released as small-press editions, the full collection is now available online. (Steve Jobs Archive)

Statement from Dario Amodei on our discussions with the Department of War. Anthropic drew two lines it won’t cross: mass domestic surveillance and fully autonomous weapons. Under threat from the DoD, Anthropic refused. Anti-mass surveillance and anti-autonomous murderbots are moral red lines to have, and I’m glad they didn’t cave. On Friday, the administration indeed canceled the contract and labeled Anthropic as a “supply-chain risk to national security,” a designation reserved for adversarial foreign companies. (Dario Amodei / Anthropic)

SaaS Isn’t Dead. But the Way You Used to Win in B2B? That’s Gone. The old SaaS playbook—lock up a category early, grind to $100M ARR over five years, coast on inertia—is done. AI-native startups are reaching $100M in twelve months with 50-person teams, shipping weekly, and winning on ROI instead of switching costs. Incumbents who haven’t shipped meaningful improvements in two years are watching customers finally find a reason and a path to leave. (Jason Lemkin / SaaStr)

I published “Product Design Is Changing“ on Monday. LinkedIn largely agreed. Reddit was hostile. A former creative director I worked with years ago left a comment: “So much for years of craft and imagination... I didn’t sign up for this.”

He’s right. None of us signed up for it. But the reactions kept splitting along the same fault line: people who agree the shift is happening but are still opening Figma every morning and drawing pictures of software. That’s the thing I’ve been chewing on all week—why the default workflow is so sticky even when everyone can see it changing.

For decades, the job has been: design in one tool, hand off to engineers who rebuild it in another. We draw pictures of apps. We sweat over pixels in those pictures. Then someone else translates them into the thing that actually ships. Every stage of that handoff generates waste—the alignment meetings, the redlines, the QA passes comparing mockups to code. Laura Klein’s NN/g piece on empowered teams catalogs where all that overhead lands: PMs spending 70% of their time coordinating, fragmented squads producing products that feel like they were designed by strangers. That’s what happens when the picture and the product are two different artifacts maintained by two different groups of people.

Some companies have stopped doing it that way entirely. At Intercom, every designer now ships code to production. Zero did 18 months ago. The CPO’s test for any role: what would a startup founded today do here? Over at Anthropic, teams group-sculpt living prototypes with no spec and no roadmap beyond 90 days—and shipped Claude Cowork ten days after someone first had the idea. Dan Shipper’s Every runs four products with single-person teams and 99% AI-written code. Amazon’s two-pizza team just became a two-slice team. In all of these, the designer works in the final medium. No pictures. No handoff.

So why is everyone else still drawing? Part of it is that the advice hasn’t caught up. A UX Magazine piece told designers to sharpen their critical thinking and be the conscience in the room—the kind of thing you can agree with without changing anything about your day-to-day. Jan Tegze’s piece on job shrinkage gets at why that advice doesn’t land. He quotes a CEO: “Our senior people and our junior people are equally lost when we ask them what we should do. The seniors are just more articulate about their uncertainty.” The mockup workflow felt like the job. Letting go of it means admitting that a lot of what filled the day was production, not strategy.

The piece I’d actually hand a designer is Tommaso Nervegna’s Claude Code guide, which shows what working in the final medium looks like in practice: spec-driven development through conversation with an AI agent, design process applied to code instead of Figma. The designer’s value is in the questions asked before any code gets written. That’s the craft now—not the mockup, but the specification and the judgment call.

Matt Shumer wrote a piece saying that the disruption tech workers are living through is heading for every knowledge-work profession. Design sits in an interesting middle—the interfaces people see and touch still need human judgment, but the production layer underneath keeps compressing.

Here’s what I keep coming back to. A designer can read this newsletter, agree with all of it, and still open Figma on Monday and draw pictures of software exactly the way they did last year. The shift demands more than updated beliefs. It demands picking up the actual material.

ASCII Me

Over the past couple months, I’ve noticed a wave of ASCII-related projects show up on my feeds. WTH is ASCII? It’s the basic set of letters, numbers, and symbols that old-school computers agreed to use for text.

I think it’s sort of a halo effect from Claude Code and the nostalgia designers and developers have for text-based terminals. Anyway, I wrote a roundup of stuff that’s caught my eye.

Read the article

What I’m Consuming

Frames That Force Us to Look. A piece connecting the 1972 Napalm Girl photo to the image of five-year-old Liam Conejo Ramos being taken by ICE agents—and why still photographs move public opinion in ways video can’t. Deb Aldrich traces the lineage through Kent State and Pete Souza’s Obama-era work, then warns that government-manipulated images are actively undermining photographic trust. (Susan Milligan / PRINT Magazine)

Agentic Lovemarks: How Brands Can Top Both Human and AI-Driven Shortlists. Arjan Kapteijns builds on Thomas Marzano’s Brand Constitutions manifesto to ask what happens to Kevin Roberts’ classic Lovemarks framework when AI agents start mediating brand discovery. His argument: brands now need to speak to two audiences simultaneously—the humans whose hearts they want to win and the intelligent systems acting on those humans’ behalf. Love stays human, but respect has to become machine-readable. (Arjan Kapteijns / Brandingmag)

These 3 ‘Addictive’ Social Media UX Features Are on Trial. Grace Snelling covers the Los Angeles lawsuit arguing that infinite scroll, ephemeral content, and algorithmic recommendations are deliberately engineered to be addictive—especially to kids. Snap and TikTok already settled; Meta and Google are the remaining defendants. The legal theory frames social media UX as a public nuisance, borrowing from the playbook used against the cigarette industry. (Grace Snelling / Fast Company)

Who Cares if Matt Damon’s ‘Odyssey’ Helmet Is Historically Accurate? (Gift link) Paul McAdory examines why audiences nitpick historical accuracy in film adaptations of Homer and Brontë, arguing that the obsession with fidelity reflects something deeper—a desire for stable reference points in an unstable present. When you can’t control where the world is heading, policing whether Odysseus has the right helmet becomes a strange kind of comfort. (Paul McAdory / The New York Times)

I’ve been thinking about what actually makes someone good at their job. Not competent, but good. The kind of good where you look at a screen and know something is off before you can articulate why. The kind where you can tell a product decision is wrong from across the room.

Christina Wodtke calls this “compressed experience”—years of pattern recognition folded into a split-second gut reaction. A seasoned designer isn’t being mystical when they sense a flow is broken. They’ve just done it enough times that the signals are automatic. And the only way to build that compression is reps. Not reading about reps. Actual reps.

Peter Yang landed on the same idea from a different direction. His 25 product beliefs after a decade at Roblox, Reddit, Amazon, and Meta boil down to a claim that makes credentialists uncomfortable: the only thing that matters is what you’ve shipped and your ideas to improve the product. He estimates fewer than 10% of PMs actually dogfood their own product weekly. If you’re not paying attention to the thing you ship, you’re not building judgment. You’re just accumulating tenure.

Now add AI to this. Anthropic published a randomized controlled trial on junior engineers learning a new Python library. The group using AI assistance scored 17% lower than those who coded by hand. They didn’t finish meaningfully faster, either. The biggest gap was in debugging—the exact skill you need most when your job is overseeing AI output. Anthropic’s researchers said it plainly: AI may accelerate productivity on skills you already have while hindering the acquisition of new ones.

Daniel Miessler put this more viscerally, riffing on a Karpathy interview about entropy. Adults “collapse” over time—revisiting the same thoughts, narrowing their aperture, ossifying. AI makes it worse. You’re outsourcing your thinking to a system that learned by averaging the internet. The outputs pass a first glance. But nothing in there will surprise anyone because the model optimizes for the most statistically probable next token.

So how do you stay sharp while using the tools? Daniel Rosenberg has one approach: design the conceptual model before you touch a screen. Objects are nouns. Actions are verbs. Attributes are adjectives. Your interface is a language before it’s a layout, and if you get the grammar wrong, no amount of visual polish saves you. Jumping straight to screens means making those language decisions implicitly, without realizing it. Rosenberg’s argument is that you should write the grammar first—literally work in text before pixels.

Patrick Morgan comes at it differently: externalize your reasoning in plain text so AI can actually work with it. Three Markdown files—process, taste, raw thinking. Your judgment has to exist somewhere outside your head, and it has to be legible. Designers externalize visual thinking all the time with moodboards and component libraries. But we rarely write down why we made the choices we made. Do that, and AI amplifies your judgment instead of replacing it.

Erika Flowers makes the organizational version of this argument. On the Invisible Machines podcast, she compares AI adoption to roofing: before you can install anything, you spend a week building scaffolding, setting up tarps, rigging safety harnesses. Nobody wants to fund that part. Everyone wants the flashy use case. But the scaffolding—data integration, governance, connected workflows—is what makes the use case actually work.

All of it comes back to the same thing. The slow, sometimes painful work that builds real understanding is the work AI makes easiest to skip. And skipping it has a cost that doesn’t show up until later—when you’re debugging output you don’t understand, or shipping a product you can’t explain, or deploying an AI feature on top of infrastructure that was never ready for it.

I keep coming back to a line from Anthropic’s study: “Cognitive effort—and even getting painfully stuck—is likely important for fostering mastery.” Getting stuck is the apprenticeship. The reps are the point. Use AI to do more of them, not fewer.

What Wall Street Gets Wrong About SaaS and What’s Next

I wrote two pieces on the SaaS market. What Wall Street Gets Wrong About SaaS makes the case that AI won't kill enterprise software, for the same reason Zapier didn't kill systems integrators and Squarespace didn't kill web designers—capability doesn't equal DIY, and every wave that was supposed to eliminate professional work ended up expanding the market instead. Then in What's Next in Vertical SaaS, I pulled that thread forward with Charlie Warren, Eli Dukes, and my colleague Duncan Grazier: if SaaS survives, where does defensibility live next? Their answer is the data recipe—the specific chain of decisions, intent, and context that turns raw information into economically valuable results. The model layer isn't the moat anymore. The thinking behind the output is.

What I’m Consuming

A History of the Permalink. In early 2000, Jason Kottke added permanent links to individual blog posts, which Caroline Van Oosten highlighted on prolific.org and helped popularize the idea of persistent URLs. Folks at Blogger, Matt Haughey, Evan Williams, and Paul Bausch developed a code solution using anchor IDs to link to single entries, enabling true permalinks across blogs. By late March 2000, Bausch published an official guide, and the concept spread, ultimately evolving into what we now call permalinks. (Jay / The History of the Web)

Does Software Piracy Exist? Matthew Butterick, a type designer, argues the optimal level of software piracy is greater than zero. Zero piracy means either you never shipped or you gave it away free—both produce $0 in revenue. Maximum revenue happens in the middle, where some piracy occurs. And most people downloading your fonts from pirate sites were never going to pay anyway. (Matthew Butterick)

The Moylan Arrow of Software. After the death of James Moylan, the designer of that little arrow on your fuel gauge showing which side the gas cap is on, Marcin Wichary asks: what’s the software equivalent? His pick is iOS’s Security Code AutoFill—it benefits everyone, solves a real friction, shows up at the right time, and once you know it’s there, you love it forever. (Marcin Wichary / Unsung)

Wes Cook and the Centralia McDonald’s Mural. The full story behind Cabel Sasser’s XOXO 2024 talk: a decade-long effort to save a hand-painted McDonald’s mural from a remodel, a canvas that peeled right off the wall, a hidden “COMING SOON!” message from 1980, and a suspiciously detailed cow udder that nearly triggered a lawsuit. A wonderful read. (Cabel Sasser)

This week, Anthropic shipped new plug-ins for Claude Cowork—the kind that review contracts and run financial analysis, work that software companies have built entire businesses around. The stock market noticed. Salesforce, Intuit, and ServiceNow all dropped double digits; the IGV software index is down roughly 30% from its September peak. If AI can do the work directly, the tools that organized the work are worth less. Or so the market thinks.

That’s the macro story. The micro version has been showing up in everything I linked to this week. In the not-so-distant past, the startups that survived were the ones that shipped fast. Build and deploy faster than your competition, you won. That was the moat. With Claude Code, Codex, Antigravity and other agentic coding tools, teams can now ship in weeks what used to take quarters. And the result is that a lot of teams are building the wrong things faster than ever.

Gale Robins opened the week with a scene I’ve watched play out from both sides of the table: a team ships three features on time, hits all their velocity metrics, and can’t answer the question “what problem do these features solve?“ She walks through 19 specific judgment points in the product discovery process where human decisions determine whether teams build the right thing or waste months on the wrong one. Her point: AI didn’t eliminate the need for those decisions. It just compressed the timeline so the cost of skipping them shows up faster. Christina Wodtke made the same case from the research side—a designer spent two weeks vibe-coding a gratitude journaling app with confetti animations and gentle notifications, showed it to users, and learned that nobody journals. Two weeks building the wrong thing. Wodtke’s advice: sit down with five to ten people, shut up, and listen. You’ll build less. It’ll be the right thing.

Nielsen Norman Group’s annual State of UX report extended this to the interface itself. Design systems standardized the components; AI-mediated interactions sit on top of the screen. Polished UI is no longer a differentiator. Anyone can produce a decent-looking interface now. Kai Wong interviewed 22 design leaders who all said some version of the same thing: a PM went to an AI tool and came back with something that looked 90% done to an untrained eye. The superpower didn’t disappear. It just stopped being rare enough to carry a career.

So where does our value as designers go? Deeper. Into the work that’s hardest to see and hardest to automate: research, judgment, the ability to articulate why this interface works and tie that explanation to a business outcome. I see this on my own team. The designers who are hardest to replace aren’t the ones who produce the most polished screens. They’re the ones who can walk into a room with a PM and an engineer, explain why something needs to change, and connect that explanation to what users actually do. That skill has always mattered, but it used to be a nice-to-have on top of solid craft. Now it is the craft.

The trouble is that this kind of work is almost invisible. I linked to a second piece this week by Kai Wong about making strategic design work legible to the people who control headcount and budgets. His advice is to stop presenting design decisions in design terms—don’t explain that “Option A follows the Gestalt principle of proximity”; say it reduces checkout from five steps to three. Hardik Pandya made the same argument about coordination work more broadly—the person who writes the doc that gives a project its shape, who closes context gaps in one-on-ones before they become conflicts, never shows up in the launch email. Credit flows to the people whose contributions are easy to describe. The invisible work is the hardest to defend in a budget meeting, and it’s exactly the work that matters most now.

Meanwhile, the posts about AI interfaces kept circling the same drain. Katya Korovkina argued that chatbot-first thinking is leading teams to ship worse experiences than what they’re replacing—prompt-based products work best for users who already know how to ask the right question, which excludes a lot of people. Julian Scaff reframed Fitts’s Law for AI: the friction didn’t disappear, it just moved from the screen to the gap between what a user intends and what the system understands. When a user stares at a blank text field and doesn’t know what to type, that’s distance. And Tushar Deshmukh described enterprise teams whose AI-powered dashboards got ignored because they violated twenty years of cognitive routine. The fix in every case was the same: layer the new on top of the familiar. Which requires knowing what’s familiar. Which requires research. There’s no shortcut.

Andy Coenen’s isometric NYC project put it most concisely from the creative side: “If you can push a button and get content, then that content is a commodity. Its value is next to zero.” When the hard parts become easy, the differentiator becomes love—and love is just another word for the judgment, care, and depth that no model can generate on its own.

The optimistic read on all of this is that the skills designers have always claimed to value—empathy, research, strategic thinking—are now the only ones that matter. The uncomfortable read is that a lot of us have been coasting on execution skills that are rapidly losing their premium. Wall Street looked at the same dynamic this week and concluded that software companies are finished. I’m not so sure.

Most businesses don’t want to be in the business of building and maintaining software—that is not their core competency. They’d rather continue to make widgets than software to support making widgets. The SaaS companies that survive will be the ones that understand this: their value is not the software. It’s the business outcomes the software enables. As designers, we must understand the same: our worth is not in crafting polished UIs, but building experiences that unlock real business value for clients.

OpenClaw and the Agentic Future

Recently, an autonomous AI agent named OpenClaw (fka Clawd, fka Moltbot) took the tech community by storm, including a run on Mac minis as enthusiasts snapped them up to host OpenClaw 24/7. In case you’re not familiar, the app is a mostly unrestricted AI agent that lives and runs on your local machine or on a server—self-hosted, homelab, or otherwise. What can it do? You can connect it to your Google accounts, social media accounts, and others and it can act as your pretty capable AI assistant. It can even code its own capabilities. You chat with it through any number of familiar chat apps like Slack, Telegram, WhatsApp, and even iMessage.

Feeling some real FOMO, I decided to give it a try.

Read the full essay

What I’m Consuming

How OpenClaw's Creator Uses AI to Run His Life in 40 Minutes. Peter Steinberger argues his creation could become a personal operating system, but cautions against “slop town” agent orchestration and emphasizes keeping a human in the loop and learning through iteration. Peter Yang interviews him for his podcast. (Peter Yang)

On the Inevitability of Design Changing Forever. Andrew Boardman outlines three possible futures for graphic design in the AI age: (1) a fully democratic design era where AI-powered platforms democratize production and designers become efficiency-focused engineers; (2) the death of design, with AI replacing most design work and traditional roles vanishing; and (3) design as a defiant, craft-driven practice where independent designers thrive despite broader upheaval. He cites signs like shrinking entry-level opportunities and AI tools enabling rapid tool-building, arguing that change is inevitable. (Andrew Boardman / Dear Designer)

The Hidden History of Women Game Designers. Educational board and card games in the late eighteenth and nineteenth centuries opened a rare professional and intellectual space for women, especially in music education, where they designed elaborate games to teach theory concepts through play. A fascinating read. (Carmel Raz / JSTOR Daily)

Times New Resistance. While I don’t condone modifying someone else’s computer without them knowing, this is a mischievous/funny/interesting piece of software as art by Abby Haddican. She’s created a version of Times New Roman that “autocorrects the autocrats.” When someone types certain oft-used MAGA terms, their computer will turn those words and phrases into liberal resistance terms. For example, when installed, this font will turn “Stephen Miller” into “Nosferatu” and “Mar-a-Lago” into “the Fourth Circle of Hell.” (Abby Haddican)

This week’s big idea emerged from different angles: the best tools come to you.

It was really Daniel Kennett who came up with the idea in his retrospective on Aperture. Apple’s discontinued prosumer photo app let you edit images wherever you were inside the program—on the map view, in its book editor, while browsing on the light table. You didn’t have to context-switch into “editing mode” like Adobe Lightroom. The tool came to you. A decade later, nothing matches it, and Apple replaced it with something demonstrably worse (Photos).

Aperture failed commercially, but the principle didn’t. Anil Dash’s history of Markdown tells the success version of the same story: a plain-text format John Gruber made up in 2004 now powers the trillion-dollar AI industry. Markdown won because it’s invisible infrastructure—it adapts to how people already write instead of demanding they learn an involved new system—just a little syntax. Dash’s line—”the internet gets built by smart people who think of good ideas, then give them away freely, until they slowly take over the world”—is really a story about tools that refuse to get in the way.

That same humility showed up in how Anthropic shipped Claude Cowork in 10 days. They watched how people actually used Claude Code—planning vacations, building presentations, controlling ovens—and built for that behavior instead of correcting it. The product emerged from observation, not prescription. When your users show you what they need, the move is to follow, not redirect.

The implication for design work is significant. Luke Wroblewski’s insight, which I covered in design tools as the new deliverables, is that brand guidelines should become software—systems that generate on-brand assets on demand rather than static PDFs that get ignored or misinterpreted. The deliverable meets you when you need it, in the format you need it.

There’s a dark mirror to all of this. Joe Gebbia’s tenure as Chief Design Officer in the Trump administration is design that imposes rather than adapts—promotional websites for political initiatives while USDS and 18F, the agencies that actually made government services usable, get gutted. Paula Scher’s point stands: you can’t make government “delightful” while stripping food stamps. That’s design serving the administration, not the user. The opposite of meeting people where they are.

Markdown won. Aperture is still mourned a decade later. Claude Cowork shipped in 10 days because someone was paying attention. The best UX comes to you.

What I’m Consuming

Streets of Minneapolis. Just the other day, I was lamenting to my teenage son—who’s into hip-hop—how there needs to be more protest songs from musical artists, especially right now. Then Bruce Springsteen released “Streets of Minneapolis,” which apparently he wrote and recorded in just a couple of days. Gotta love The Boss. And while this is admirable, hearing from younger artists who’ll reach a younger demographic would be nice. (Bruce Springsteen)

Where all the protest songs are. This morning, NPR’s music critic Ann Powers sent out a newsletter answering the question I posed above. Her point is two-fold: many old guard musicians like Springsteen, Billy Bragg, Dave Matthews, and Lucinda Williams are releasing protest songs; and so are many indie folk artists. She details a lot of who they are and where to find them. (Ann Powers / NPR)

The Adolescence of Technology. In this follow-up to “Machines of Loving Grace,” Anthropic CEO Dario Amodei outlines the profound risks humanity faces with rapidly advancing “powerful AI,” categorizing them into autonomy, misuse for destruction, misuse for seizing power, economic disruption, and indirect effects. He argues that humanity is entering a turbulent rite of passage that demands careful risk assessment, thoughtful regulation, and proactive measures. Amodei proposes a multi-faceted “battle plan” involving AI alignment, interpretability, transparency, and judicious societal interventions like chip export controls and progressive taxation to navigate these challenges and ensure AI benefits humanity. (Dario Amodei)

Text is king. Adam Mastroianni challenges the popular narrative that digital technology is leading to the demise of reading and complex thought. He argues that while some data indicates a slight decline in reading time, book sales and independent bookstores are actually thriving, suggesting that text remains a vital medium. Mastroianni asserts that human desires for deep intellectual engagement are not easily satisfied by fleeting digital content, and that writing is indispensable for processing and preserving complex ideas. Although, I’d argue that we all need to pick up a novel every now and again—myself included. (Adam Mastroianni / Experimental History)

Project Hail Mary. Speaking of novels, I just finished reading this one from Andy Weir, author of The Martian, which was turned into a movie starring Matt Damon. Project Hail Mary is a page-turner and oftentimes laugh-out-loud funny despite the apocalyptic premise. It’s coming out as a movie in March with Ryan Gosling playing the main character. (Andy Weir)

The optimistic case for designers in an AI-driven world is that design becomes strategy. When apps get reduced to APIs and primitives, someone still has to define what the product is. Jeff Veen makes this case directly in his piece on coding agents and the future of design: “An agentic future elevates design into pure strategy, which is what the best designers have wanted all along.”

But are designers actually capturing that strategic role?

Lenny’s Newsletter published survey results from 1,750 tech workers in late December, and the findings by role tell a different story. Designers report the lowest ROI from AI tools—45% positive, compared to 78% of founders. Meanwhile, founders are using AI to think—for decision support, product ideation, and strategy. They treat it as a thought partner, not a production tool.

Here’s the uncomfortable detail: PMs now rank prototyping as their #2 use case for AI, ahead of designers. Figma is teaching PMs to build prototypes instead of PRDs, going from idea to interactive demo without waiting on design. “The PMs who thrive,” Emma Webster writes, “will be those who embrace real-time iteration, moving fluidly across traditional role boundaries.” Traditional role boundaries being design’s territory.

And it’s not just PMs. Claude Code is taking the AI world by storm, and regular people are now building micro apps instead of buying software—fleeting, personal tools assembled by AI for problems that used to require a designer or developer.

So the strategic future is available—but designers aren’t capturing it at the same rate as other roles. Why?

Dolphia’s diagnosis in the craft crisis piece is sobering: “We told designers they didn’t need technical knowledge. Then we eliminated their jobs when they couldn’t influence technical decisions.” The industry spent years telling practitioners they didn’t need to understand implementation. Now those same designers can’t evaluate AI-generated output, can’t participate in architecture discussions, can’t advocate effectively when technical decisions are being made.

When Figma Sites launched, it generated 210 WCAG accessibility violations on demo sites—and designers couldn’t catch it because they didn’t know what to look for. Tools marketed as democratization actually require more technical knowledge, not less.

This connects to the debate Tommy Geoco unpacked in his video on the biggest design debate of 2025—the exchange between Karri Saarinen (designer and cofounder of Linear) and Ryo Lu (Cursor’s head of design) about Cursor’s visual editor. Geoco reframes the question perfectly: “Are you using the new speed to explore more territory, or just arriving at the same destination faster?”

That’s the question every designer should be asking. AI makes iteration cheap, but cheap iteration on the same territory isn’t progress. If you’re using Claude Code to get to obvious ideas faster, you’re missing the point. The value is in exploring territory you couldn’t reach before—and that requires understanding the medium well enough to know what’s possible.

So what is design’s value? Dan Ramsden offers a framework worth considering: abductive thinking. Deduction tells you users are dropping off. Induction tells you why. Abduction imagines new flows to fix it. Design’s contribution is translating intent through increasing fidelity—from sketch to prototype to tested solution—validating along the way. But as Yan Liu argues in his piece on taste, “The real gap won’t be between those who use AI well and those who don’t. It will be between those who already know what ‘good’ looks like before they ever open an AI tool.”

Execution was never design’s real value. But the industry acted like it was. Now that execution is commoditized, the question is whether designers have built the judgment and technical fluency to claim the strategic role that remains.

What I’m Consuming

How Browsers Work. As a designer, and as mentioned above, you should have an understanding of how browsers work. Here is a short one-pager describing the technical details. (Dmytro Krasun)

The Rippling/Deel corporate spying scandal may have taken another wild turn. The bonkers corporate spying saga, between HR tech companies Rippling and Deel continues. The Department of Justice has reportedly opened a criminal investigation into Deel over allegations that the HR startup hired a corporate spy to steal confidential information from rival Rippling, including sales leads, product roadmaps, and key employee details. The probe follows an ongoing civil lawsuit in which a former Rippling employee admitted in court to spying for Deel, while Deel denies wrongdoing, accuses Rippling of a smear campaign, and both companies continue countersuits and legal maneuvers involving high-profile attorneys. Despite the escalating legal and criminal scrutiny, investors have continued to back both companies at multibillion-dollar valuations. (Julie Bort / TechCrunch)

Tim Cook and Sundar Pichai are cowards. Apple and Google have not removed the social media platform X from their app stores, despite Grok’s alleged violations of content policies regarding offensive and exploitative deepfake images. This inaction is linked to concerns about potential backlash from Elon Musk and political implications, suggesting a compromise of their stated content moderation principles. (Elizabeth Lopatto / The Verge)

I’m trying a new format. A lot of times, when I’m reading a random assortment of articles across dozens of sources, I make connections. I connect the dots. This week on the blog, I linked to a bunch of articles about metrics—cold, hard numbers that quantify the outcomes we create with our design work. That’s this week’s big idea…

The Big Idea: Measuring What Matters

We’re drowning in data but starving for insight.

Product teams track everything now. Dashboards multiply. Every feature ships with its own set of KPIs. And yet, despite all this measurement, teams still struggle to answer basic questions: Is this working? Are we building the right thing? How do we know?

The problem isn’t a lack of data. It’s a lack of clarity.

Shipping faster doesn’t help if you don’t know why you’re shipping. AI tools promise to accelerate everything—PRDs, designs, code—but speed without direction is just expensive motion. The fastest teams aren’t the ones shipping the most. They’re the ones who understand what problem they’re solving and for whom. That understanding has to come first, before the metrics, before the dashboards, before the sprint planning.

Once you have that clarity, where do you look for signal? Not where most teams look. Surveys and interviews feel productive, but there’s a persistent gap between what users say and what they actually do. We all want to seem more competent than confused. Our memories are fuzzy about routine tasks. So when someone tells you what they want, they’re often solving for their own experience in ways that don’t generalize. The better approach: watch behavior, measure outcomes, and use what people say to add context—not to drive decisions.

But here’s where it gets uncomfortable. Even when we measure the right behaviors, some of the most important things resist quantification entirely. W. Edwards Deming put it plainly: the most important figures needed for management are unknown and unknowable. That doesn’t mean we ignore them. It means we stop pretending that ROI calculations capture the full value of good design. Sometimes the most powerful question you can ask leadership is the simplest one: How would you grade this product? When was the last time anyone just held it in their hands and looked?

When measurement does make sense, it should start with the user’s problem—not your feature list. The TARS framework offers a useful structure: What percentage of users have this problem? How many adopted your solution? How many stuck with it? How satisfied are they? It’s a way to connect what customers experience with what you’re tracking in the product.

Zoom out further and you need something to orient the whole product. A North Star Metric defines the relationship between the problems you’re solving and the value you’re creating. But picking the right one depends on knowing what game you’re playing. Are you competing for attention, transactions, or productivity? Different games demand different measures of success.

The throughline: data is a tool, not a destination. Clarity comes first. Watch what users do, not just what they say. Accept that some things can’t be captured in a dashboard. And when you do measure, make sure you’re measuring what actually matters—not just what’s easy to count.

Directing AI: How I Made an Animated Holiday Short

In my last email, I teased that I was working on something in ComfyUI:

I’ve been playing a lot with ComfyUI recently—I’m working on a personal project that I’ll share if/when I finish it. But it made me realize that prompting a visual to get it to match what I have in my mind’s eye is not easy.

It’s an animated short and I “premiered” it for my family on December 30th:

I’ve spent most of my career working with world-class human artists—animators at Pixar, art directors, motion graphics artists, and illustrators at ad agencies—so I was not entirely sure this could work or if I could pull it off. Over two months, I wrestled with tangles of ComfyUI noodles to direct a small army of AI video models to make a two-and-a-half‑minute holiday short about the thing I care about most: my family. I wrote up a story of how I did it—and what it really means to “direct” AI instead of letting it direct you.

The Year AI Changed Design

At the beginning of 2025, AI was still something designers were poking at from the edges—cute demos, promising tools, lots of hype. Twelve months later, it’s sitting in the middle of our work: in our research, our explorations, our prototypes, and in some cases, our production code. The fundamentals of design haven’t changed, but how we get from problem to product has—and 2025 was the year that shift became impossible to ignore. Here’s my wrap-up.

Something Fun

MTV Rewind

“I want my MTV!” That is the line that many music artists spoke to camera in a famous campaign by George Lois to get fans to call their cable companies to ask for MTV. It worked.

While MTV’s international music-only channels went off the air at the end of 2025, its US channels still exist. They’re just not all-music all the time like it was in the 1980s.

That’s where MTV Rewind comes in. It’s a virtual TV where you can relive MTV programming as it was. Built by an artist going by FlexasaurusRex, it’s an archive of Day 1 programming, and then different channels (YouTube playlists) to shuffle through the different shows, including 120 Minutes.

What I’m Consuming

Top 10 UX Articles of 2025. Good reference overall. This piece highlights the most-read NN/g UX articles of 2025, which focus heavily on how AI is reshaping UX practice, skills, and careers, alongside enduring usability fundamentals like button states, navigation patterns, and emerging visual styles. It also surfaces a bonus set of popular 2024 articles that deepen into AI for research, synthetic users, prompt design, and the limits and risks of generative chatbots. (NN/g)

The Enshittifinancial Crisis. Ed Zitron, in a 19,000-word opus argues that the AI boom is not a real, productive revolution but a massive, debt- and venture-fueled bubble that extends “enshittification” into finance itself, with banks, VCs, hyperscalers, and data-center builders all propping up unprofitable AI companies and opaque GPU spending to keep stock prices rising. He warns that once cheap capital and investor credulity run out, AI startups, data-center projects, and even parts of the broader market will implode, exposing how little genuine value and profitability lie beneath today’s AI hype. (Ed Zitron)

Click: How to Make What People Want. Jake Knapp, the designer who brought us the design sprint is back with a new book and new process. Click is a practical guide to starting big projects using a two-day “Foundation Sprint” that helps teams clarify their ideas, differentiate from competitors, and make smart decisions fast. It builds on the original Sprint framework with 10 lessons, a step‑by‑step playbook, and case studies from companies like Google, Nike, and Slack so you can quickly test a founding hypothesis and set your team up to win from day one. (Jake Knapp with John Zeratsky)

Early last week I listened to an episode of Design of AI which featured Dr. Maya Ackerman is wonderful. She echoed a lot of what I’ve been thinking about recently—how AI can augment what we as designers and creatives can do. There’s a ton of content out there that hypes up AI and how it can do the job of a specialist like a marketer or designer. They proclaim, just “Type this prompt and instantly get a marketing plan!” or “Type this prompt and get an entire website!”

Ackerman, as interviewed by Arpy Dragffy-Guerrero:

I have a model I developed which is called “humble creative machines” which is [the] idea that we are inherently much smarter than the AI. We have not reached even 10% of our capacity as creative human beings. And the role of AI in this ecosystem is not to become better than us but to help elevate us. That applies to people who design AI, of course, because a lot of the ways that AI is designed these days, you can tell you’re cut out of the loop. But on the other hand, some of the most creative people, those who are using AI in the most beneficial way, take this attitude themselves. They fight to stay in charge. They find ways to have the AI serve their purposes instead of treating it like an all-knowing oracle. So really, it’s sort of the audacity, the guts to believe that you are smarter than this so-called oracle, right? It’s this confidence to lead, to demand that things go your way when you’re using AI.

Her stance is that those who use AI best are those that wield it and shape its output to match their sensibilities. And so, as we’ve been hearing ad nauseam, our taste and judgement as designers really matters right now.

I’ve been playing a lot with ComfyUI recently—I’m working on a personal project that I’ll share if/when I finish it. But it made me realize that prompting a visual to get it to match what I have in my mind’s eye is not easy. This recent Instagram reel from famed designer Jessica Walsh captures my thoughts well:

I would say most AI output is shitty. People just assumed, “Oh, you rendered that an AI.” “That must have been super easy.” But what they don’t realize is that it took an entire day of some of our most creative people working and pushing the different prompts and trying different tools out and experimenting and refining. And you need a good eye to understand how to curate and pick what the best outputs are. Without that right now, AI is still pretty worthless.

It takes a ton of time to get AI output to look great, beyond prompting: inpainting, control nets, and even Photoshopping. What most non-professionals do is they take the first output from an LLM or image generator and present it as great. But it’s really not.

So I like what Dr. Ackerman mentioned in her episode: we should be in control of the humble machines, not the other way around.

Highlighted Links

Silicon clay: how AI is reshaping UX design

Andrew Tipp does a deep dive into academic research to see how AI is actually being used in UX. He finds that practitioners are primarily using AI for testing and discovery: predicting UX, finding issues, and shaping user insights.

The highest usage of AI in UX design is in the testing phase, suggests one of our 2025 systematic reviews. According to this paper, 58% of studied AI usage in UX is in either the testing or discovery stage. This maybe shouldn’t be surprising, considering generative AI for visual ideation and UI prototyping has lagged behind text generation.

But, in his conclusion, Tipp echoes Dr. Maya Ackerman’s notion of wielding AI as a tool to augment our work:

However, there are potential drawbacks if AI usage in UX design is over-relied on, and used mindlessly. Without sufficient critical thinking, we can easily end up with generic, biased designs that don’t actually solve user problems. In some cases, we might even spend too much time on prompting and vibing with AI when we could have simply sketched or prototyped something ourselves — creating more sense of ownership in the process.

2025: The State of Generative AI in the Enterprise

There’s a lot of chatter in the news these days about the AI bubble. Most of it is because of the circular nature of the deals among the foundational model providers like OpenAI and Anthropic, and cloud providers (Microsoft, Amazon) and NVIDIA.

OpenAI recently published a report called “The state of enterprise AI” where they said:

The picture that emerges is clear: enterprise AI adoption is accelerating not just in breadth, but in depth. It is reshaping how people work, how teams collaborate, and how organizations build and deliver products.

AI use in enterprises is both scaling and maturing: activity is up eight-fold in weekly messages, with workers sending 30% more, and structured workflows rising 19x. More advanced reasoning is being integrated— with token usage up 320x—signaling a shift from quick questions to deeper, repeatable work across both breadth and depth.

Investors at Menlo Ventures are also seeing positive signs in their data, especially when it comes to the tech space outside the frontier labs:

The concerns aren’t unfounded given the magnitude of the numbers being thrown around. But the demand side tells a different story: Our latest market data shows broad adoption, real revenue, and productivity gains at scale, signaling a boom versus a bubble.

AI has been hyped in the enterprise for the last three years. From deploying quickly-built chatbots, to outfitting those bots with RAG search, and more recently, to trying to shift towards agentic AI. What Menlo Venture’s report “The State of Generative AI in the Enterprise” says is that companies are moving away from rolling their own AI solutions internally, to buying.

In 2024, [confidence that teams could handle everything in-house] still showed in the data: 47% of AI solutions were built internally, 53% purchased. Today, 76% of AI use cases are purchased rather than built internally. Despite continued strong investments in internal builds, ready-made AI solutions are reaching production more quickly and demonstrating immediate value while enterprise tech stacks continue to mature.

Also startups offering AI solutions are winning the wallet share:

At the AI application layer, startups have pulled decisively ahead. This year, according to our data, they captured nearly $2 in revenue for every $1 earned by incumbents—63% of the market, up from 36% last year when enterprises still held the lead.

On paper, this shouldn’t be happening. Incumbents have entrenched distribution, data moats, deep enterprise relationships, scaled sales teams, and massive balance sheets. Yet, in practice, AI-native startups are out-executing much larger competitors across some of the fastest-growing app categories.

How? They cite three reasons:

• Product and engineering: Startups win the coding category because they ship faster and stay model‑agnostic, which let Cursor beat Copilot on repo context, multi‑file edits, diff approvals, and natural language commands—and that momentum pulled it into the enterprise.

• Sales: Teams choose Clay and Actively because they own the off‑CRM work—research, personalization, and enrichment—and become the interface reps actually use, with a clear path to replacing the system of record.

• Finance and operations: Accuracy requirements stall incumbents, creating space for Rillet, Campfire, and Numeric to build AI‑first ERPs with real‑time automation and win downmarket where speed matters.

There’s a lot more in the report, so it’s worth a full read.

What I’m Consuming

What ‘67’ Reveals About Childhood Creativity. My Gen Z kids are sick of “6-7.” If you’ve been under a rock, the phrase is a contemporary trend among Gen Alpha that reflects a long-standing tradition of children’s folklore and vernacular. This phenomenon demonstrates that children continue to create and share their own unique language and memes, adapting them rapidly in the digital age, similar to historical patterns. (Allegra Rosenberg / Atlas Obscura)

The Reverse-Centaur’s Guide to Criticizing AI. The current AI narrative emphasizes job replacement, creating an investment bubble driven by tech companies’ need for continuous growth. Cory Doctorow argues that AI tools are designed to create “reverse centaurs” (humans serving machines) rather than assist humans, and critiques the expansion of copyright as a solution for creative professionals. Instead, it proposes advocating for sectoral bargaining rights to protect workers against AI and market monopolies. (Cory Doctorow / The Pluralistic)

The Spark. Fun, immersive—but short—interactive story by Digital Panda, digital design agency. Reminds me of something that would have been featured as a site of the day on The FWA back in the Flash days. (Digital Panda)

According to recently unsealed court documents, Meta discontinued its internal studies on Facebook’s impact after discovering direct evidence that its platforms were detrimental to users’ mental health. In a 2020 research project code-named “Project Mercury,” Meta scientists found that people who stopped using Facebook for a week reported lower feelings of depression, anxiety, and loneliness. Rather than publishing those findings or pursuing additional research, Meta called off further work and internally declared that the negative study findings were tainted by the “existing media narrative” around the company.

This isn’t surprising. But it is damning.

As more evidence comes to light about Meta’s failings—and possibly criminal behavior—we as tech workers, and specifically designers making technology that billions of people use, have to do better. My previous essay was an indictment on the algorithm. But I’ve come across a couple of pieces recently that bring the responsibility closer to UX’s doorstep.

We know about infinite scroll. We know about doomscrolling. We know the attention economy generates billions in ad revenue by selling us to advertisers. But what really stings is this observation from Elvis Hsiao:

Those designers knew what they were building. Internal documents, whistleblower testimony, and the simple existence of features like screen time limits prove the companies understand the addictive nature of their products, but prioritize profits over well-being.

So what can we as designers and makers of technology do?

Perfectly timed with the release of Guillermo del Toro’s Frankenstein, Elenor Howe uses Victor Frankenstein as a parabolic tale—arguing that we’ve acted with the same hubris, building systems optimized for profit and engagement, then hiding behind diffused responsibility when systemic harms emerge. She proposes something she calls “The Architect’s Mandate”—a Hippocratic Oath for tech.

In the full essay, I break down her seven mandates and make the case for why we should cosign them. We’ve wrought a monster, but we can’t abdicate our duties any longer. We have to make things better by making better things.

➡️ Read the full essay

Highlighted Links

Critique

Critiques are the lifeblood of design. Anyone who went to design school has participated in and has been the focus of a crit. It’s “the intentional application of adversarial thought to something that isn’t finished yet,” as Fabricio Teixeira and Caio Braga, the editors of DOC put it.

A lot of solo designers—whether they’re a design team of one or if they’re a freelancer—don’t have the luxury of critiques. In my view, they’re handicapped. There are workarounds, of course. Such as critiques with cross-functional peers, but it’s not the same. I had one designer on my team—who used to be a design team of one in her previous company—come up to me and say she’s learned more in a month than a year at her former job.

Further down, Teixeira and Braga say:

In the age of AI, the human critique session becomes even more important. LLMs can generate ideas in 5 seconds, but stress-testing them with contextual knowledge, taste, and vision, is something that you should be better at. As AI accelerates the production of “technically correct” and “aesthetically optimized” work, relying on just AI creates the risks of mediocrity. AI is trained to be predictable; crits are all about friction: political, organizational, or strategic.

Ondřej Konečný | Books

Designer and front-end dev Ondřej Konečný has a lovely presentation of his book collection.

My favorites that I’ve read include:

• Creative Selection by Ken Kocienda (my review)

• Grid Systems in Graphic Design by Josef Müller-Brockmann

• Steve Jobs by Walter Isaacson

• Don’t Make Me Think by Steve Krug

• Responsive Web Design by Ethan Marcotte

The Junior Hiring Crisis

As regular readers will know, the design talent crisis is a subject I’m very passionate about. Of course, this talent crisis is really about how companies who are opting for AI instead of junior-level humans, are robbing themselves of a human expertise to control the AI agents of the future, and neglecting a generation of talented and enthusiastic young people.

Also obviously, this goes beyond the design discipline. Annie Hedgpeth, writing for the People Work blog, says that “AI is replacing the training ground not replacing expertise.”

We used to have a training ground for junior engineers, but now AI is increasingly automating away that work. Both studies I referenced above cited the same thing - AI is getting good at automating junior work while only augmenting senior work. So the evidence doesn’t show that AI is going to replace everyone; it’s just removing the apprenticeship ladder.

And then she echoes my worry:

So what happens in 10-20 years when the current senior engineers retire? Where do the next batch of seniors come from? The ones who can architect complex systems and make good judgment calls when faced with uncertain situations? Those are skills that are developed through years of work that starts simple and grows in complexity, through human mentorship.

We’re setting ourselves up for a timing mismatch, at best. We’re eliminating junior jobs in hopes that AI will get good enough in the next 10-20 years to handle even complex, human judgment calls. And if we’re wrong about that, then we have far fewer people in the pipeline of senior engineers to solve those problems.

What I’m Consuming

Is Gen X Actually the Greatest Generation? (Gift link) Disclosure, I’m Gen X, hence my obsession. Gen X’s formative years of independence, DIY culture, and ironic anti‑authoritarianism produced enduring art across music, film, TV, and magazines, now experiencing renewed appreciation. Shaped by latchkey childhoods, less parental oversight, and pre‑internet focus, the cohort’s works—spanning hip‑hop’s golden age, Black New Wave cinema, and feminist voices—privileged authenticity over market dictates and found community among peers. Ongoing debates about generational boundaries aside, the ethos—skeptical of conformity yet politically engaged—remains a compelling counterpoint to today’s hyper-mediated culture. (Amanda Fortini / The New York Times)

Are Claude and Gemini Winning the AI Race? Plus, How We’re Using the Latest Models | EP 167. For me, the most interesting segment of this episode of Hard Fork is where Kevin Roose and Casey Newton discuss their new favorite models: Google’s Gemini 3 and Anthropic’s Claude Opus 4.5. Gemini 3 is praised as a fast, reliable workhorse that meaningfully boosts day‑to‑day tasks—fact‑checking quickly, organizing timelines, sequencing sources, and pulling passages from long documents—even if it lacks much personality. Claude Opus 4.5 feels “special” to use: empathetic without flattery, consistently in the same “musical key,” and unusually strong at deep research and textual style transfer, producing prose that can convincingly mirror a writer’s voice. They see Gemini’s advantage in sheer speed and Google’s distribution, while Opus stands out for humane, aligned behavior and enterprise‑first incentives that avoid engagement‑driven quirks. Together, those traits make Gemini and Opus their two daily drivers for different strengths. (Hard Fork / The New York Times)

This past week, Google debuted their Gemini 3 AI model to great fanfare and reviews. Specs-wise, it tops the benchmarks. This horserace has seen Google, Anthropic, and OpenAI trade leads each time a new model is released, so I’m not really surprised there. The interesting bit for us designers isn’t the model itself, but the upgraded Gemini app that can create user interfaces on the fly. Say hello to generative UI.

I will admit that I’ve been skeptical of the notion of generative user interfaces. I was imagining an app for work, like a design app, that would rearrange itself depending on the task at hand. In other words, it’s dynamic and contextual. Adobe has tried a proto-version of this with the contextual task bar. Theoretically, it surfaces up the most pertinent three or four actions based on your current task. But I find that it just gets in the way.

When Interfaces Keep Moving

Others have been less skeptical. More than 18 months ago, NN/g published an article speculating about genUI and how it might manifest in the future. They define it as:

A generative UI (genUI) is a user interface that is dynamically generated in real time by artificial intelligence to provide an experience customized to fit the user’s needs and context. So it’s a custom UI for that user at that point in time. Similar to how LLMs answer your question: tailored for you and specific to when that you asked the original question.

Near the end of the short article, they point out some challenges, including usability.

Constantly changing UIs will cause usability problems. Much of users’ understanding of modern web interfaces is rooted in design standards (for example, logos are often in the top left). The more you use a website, the more familiar (and thus efficient) you become. As Gen UI alters the interface based on your needs, you could be shown a different UI every time you use a website. This constant relearning of the interface might cause frustration, especially in the beginning, as users transition from the old ways.

That’s been my concern all along. In fact, consistency is number four in Jakob Nielsen’s Usability Heuristics.

In the same genUI article, Kate Moran and Sarah Gibbons share a speculative example of a user booking a flight. The system knows that the user “never takes red-eye flights, so those are collapsed and placed at the very bottom of the list.” And that she prioritizes cost and travel time so those datapoints are displayed more prominently and the results are sorted accordingly. Can you imagine the support nightmare for UI that’s always changing? How would a support person even begin to troubleshoot?

But let’s get back to this week and Gemini.

➡️ Read the rest of the essay

Highlighted Links

Geoffrey Litt - The Future of Malleable Software

Geoffrey Litt is a design engineer at Notion. He is one of the authors at Ink & Switch of “Malleable software,” which I linked to back in July. I think it’s pretty fitting that he popped up at Notion, with the CEO Ivan Zhao likening the app to LEGO bricks.

In a recent interview with Rid on Dive Club, Litt explains the concept further:

So, when I say malleable software, I do not mean only disposable software. The main thing I think about with malleable software is actually much closer to … designing my interior space in my house. Let’s say when I come home I don’t want everything to be rearranged, right? I want it to be the way it was. And if I want to move the furniture or put things on the wall, I want to have the right to do that. And so I think of it much more as kind of crafting an environment over time that’s actually more stable and predictable, not only for myself, but also for my team. Having shared environments that we all work in together that are predictable is also really important, right? Ironically, actually, in some ways, I think sometimes malleable software results in more stable software because I have more control.

Why Call of Duty: Black Ops 7’s AI art controversy means we all lose

I wouldn’t call myself a gamer, but I do enjoy good games from time to time, when I have the time. A couple of years ago, I made my way through Hades and had a blast.

But I do know that the publishing of a triple-A title like Call of Duty: Black Ops takes an enormous effort, tons of human-hours, and loads of cash. It’s also obvious to me that AI has been entering into entertainment workflows, just like it has in design workflows.

Ian Dean, writing for Creative Bloq explores this controversy with Activision using generative AI to create artwork for the latest release in the Call of Duty franchise. Players called the company out for being opaque about using AI tools, but more importantly, because they spotted telltale artifacts.

Many of the game’s calling cards display the kind of visual tics that seasoned artists can spot at a glance: fingers that don’t quite add up, characters whose faces drift slightly off-model, and backgrounds that feel too synthetic to belong to a studio known for its polish.

These aren’t high-profile cinematic assets, but they’re the small slices of style and personality players earn through gameplay. And that’s precisely why the discovery has landed so hard; it feels a little sneaky, a bit underhanded.

“Sneaky” and “underhanded” are odd adjectives, no? I suppose gamers are feeling like they’ve been lied to because Activition used AI?

Dean again:

While no major studio will admit it publicly, Black Ops 7 is now a case study in how not to introduce AI into a beloved franchise. Artists across the industry are already discussing how easily ‘supportive tools’ can cross the line into fully generated content, and how difficult it becomes to convince players that craft still matters when the results look rushed or uncanny.

My, possibly controversial, view is that the technology itself isn’t the villain here; poor implementation is, a lack of transparency is, and fundamentally, a lack of creative use is.

I think the last phrase is the key. It’s the loss of quality and lack of creative use.

I’ve been playing around more with AI-generated images and video, ever since Figma acquired Weavy. I’ve been testing out Weavy and have done a lot of experimenting with ComfyUI in recent weeks. The quality of output from these tools is getting better every month.

With more and more AI being embedded into our art and design tools, the purity that some fans want is going to be hard to sustain. I think the train has left the station.

Dithering (Part 1)

Someone on X recently claimed they “popularized” dithering in modern design—a bold claim for a technique that’s been around since Robert Floyd and Louis Steinberg formalized it in 1976 and Bill Atkinson refined it for the original Macintosh. The design community swiftly reminded them that revival isn’t invention, and that dithering’s current moment owes more to retro-tech aesthetics meeting modern GPU pipelines than to any single designer’s genius.

Speaking of which, here’s a visual explainer by Damar Aji Pramudita that’s worth your time on how dithering actually works. Apparently it’s only part one of three.

What I’m Consuming

AI’s Dial-Up Era. Hype and heavy infrastructure spend define an early, dial‑up‑style phase of AI. Outcomes will differ by industry, hinging on how much unmet demand gets unlocked versus how fast automation boosts productivity. Radiology and historical cases in textiles, steel, and autos show a pattern: employment rises when cheaper, abundant services expand demand, then falls once demand saturates while automation keeps improving. Job categories—especially in software—will reshape, with more people doing engineering‑type work without the title. Even if many ventures fail, today’s exuberant investments will leave durable infrastructure that powers the next wave. (Nowfal / Wreflection)

Jeff Tweedy - “How to Write One Song.” Jeff Tweedy’s onstage conversation with Song Exploder’s Hrishikesh Hirway at Solid Sound 2024 explores his book How to Write One Song, extending its practical creative insights beyond songwriting to life and personal growth. (Hrishikesh Hirway / Song Exploder)

How to Write a Joke. Elliot Kalan introduces a structured “joke farming” system for generating jokes on demand, drawing from the underlying logic and parts of jokes rather than waiting for inspiration. (Roman Mars / 99% Invisible)

Like many people, I tried OpenAI’s ChatGPT Atlas browser recently. I immediately made it my daily driver, seeing if I could make the best of it. Tl;dr: it’s still early days and I don’t believe it’s quite ready for primetime. But let’s back up a bit.

The Era of the AI Browser Is Here

Back in July, I reviewed both Comet from Perplexity and Dia from The Browser Company. It was a glimpse of the future that I wanted. I concluded:

The AI-powered ideas in both Dia and Comet are a step change. But the basics also have to be there, and in my opinion, should be better than what Chrome offers. The interface innovations that made Arc special shouldn’t be sacrificed for AI features. Arc is/was the perfect foundation. Integrate an AI assistant that can be personalized to care about the same things you do so its summaries are relevant. The assistant can be agentic and perform tasks for you in the background while you focus on more important things. In other words, put Arc, Dia, and Comet in a blender and that could be the perfect browser of the future.

There were also open rumors that OpenAI was working on a browser of their own, so the launch of Atlas was inevitable.

Browser War 2.0

For anyone who lived through the early days of the web, there was the so-called Browser War, a fierce competition between Netscape and Microsoft Internet Explorer. A favorite pastime of the tech industry at the time was tracking the dwindling market share of Netscape as Microsoft (illegally) bundled IE with Windows and brute-forced its way to dominance. Google would launch Chrome in 2008 and it steadily climbed the charts until it overtook IE around 2012.

To be fair to Google, Chrome was (and still is) a good product. Unlike Internet Explorer, it was fast and uncluttered. Each tab ran in its own process, which meant if one site crashed or hung, it didn’t crash your entire browser. (Yes, that was a huge issue at the time.)

But in 2025, AI wants to bust out of the confines of its isolated tab and ride along as your sidekick as you go about your business on the internet. Context makes AI a better assistant.

So while Google slept on deeply integrating Gemini into Chrome and making it known, and Microsoft poorly marketed Copilot in Edge, upstarts like Dia and Comet came about. And now, Atlas.

Initial Impressions

The Atlas UI is minimal. Its homepage is essentially ChatGPT but the ask text field is an omnibox that can distinguish between URLs, search keywords, and questions.

Interestingly, when typing a search query like “whole foods,” Atlas will display search results first and answers second. The reverse of what Google search does these days where they’re prioritizing AI overviews over search results.

Continue reading 👉

Highlighted Links

Beyond the Machine

I must admit I’ve tried to read this essay by Frank Chimero—a script from a talk he recently gave—for about a week. I tried to skim it. I tried to fit it into a spare five minutes here and there. But this piece demands active reading. Not because it’s dense. But because it is great.

Chimero reflects on AI and his—and our—relationship to it. How is it being marketed? How do we think about it? How should we use it?

First off, Chimero starts with his conclusion. He believes we should reframe AI to be less like a tool or technology, and more like a musical instrument.

Thinking of AI as an instrument recenters the focus on practice. Instruments require a performance that relies on technique—the horn makes the sound, but how and what you blow into it matters; the drum machine keeps time and plays the samples, but what you sample and how you swing on top of it becomes your signature.

In other words, instruments can surprise you with what they offer, but they are not automatic. In the end, they require a touch. You use a tool, but you play an instrument. It’s a more expansive way of doing, and the doing of it all is important, because that’s where you develop the instincts for excellence. There is no purpose to better machines if they do not also produce better humans.

Then, he wanders off to give examples of four artists and their relationships with technology, stoking his audience—me, us, you—to consider “some more flexibility in how to collaborate with the machine in your own work, creative or otherwise.”

Read the whole piece. Curl up this mid-autumn Sunday afternoon with some hot tea and take the 20–25 minutes to read it and take it in.

The Fundamentals Problem

Chris Butler wrestles with a generations-old problem in his latest piece: new technologies shortcut the old ways of doing things and therefore quality takes a nosedive. But is it different this time with the tools available to us today?

While design is more accessible than ever, with Adobe experimenting with chat interfaces and Canva offering pro-level design apps for free, putting a tool into the hands of someone doesn’t mean they’ll know how to wield it.

Anyone can now create something that looks professional, that uses modern layouts and typography, that feels designed. But producing something that feels designed does not mean that any design has happened. Most tools don’t ask you what you want someone to do. They don’t force you to make hard choices about hierarchy and priority. They offer you options, and if you don’t already understand the fundamentals of how design guides attention and serves purpose, you’ll end up using too many of them to no end.

Butler concludes that as designers, we’re in a bind because “the pace of change is only accelerating, and it is a serious challenge to designers to determine how much time to spend keeping up.”

You can’t build foundational knowledge while chasing the new. But you can’t ignore the new entirely, or you’ll fall behind. So you split your time, and both efforts can suffer. The fundamentals remain elusive because you’re too busy keeping up. The tools remain half-learned because you’re too busy teaching [design fundamentals to clients].

Butler—nor I—know if there’s a good solution to this problem. Like I said at the start, this is an age-old problem. Friction is a feature, not a bug.

This is just the reality of working in a field that sits at the intersection of human behavior and technological change. Both move, but at different speeds. Human attention, cognition, emotion — these things change slowly, if at all. Technology changes constantly. Design has to navigate both.

And while Butler’s essay never explicitly mentions AI or AI tools, it’s strongly implied. Developers using AI tools to code miss out on the fundamentals of building software. Designers (or their clients) using AI to design face the issues brought up here. Those who use AI to accelerate what they already know, that seems to be The Way.

Inside the Superhuman effort to rebrand Grammarly

In a very gutsy move, Grammarly is rebranding to Superhuman. I was definitely scratching my head when the company acquired the eponymous email app back in June. Why is this spellcheck-on-steroids company buying an email product?

Turns out the company has been quietly acquiring other products too, like Coda, a collaborative document platform similar to Notion, building the company into an AI-powered productivity suite.

So the name Superhuman makes sense.

Grace Snelling, writing in Fast Company about the rebrand:

[Grammarly CEO Shishir] Mehrotra explains it like this: Grammarly has always run on the “AI superhighway,” meaning that, instead of living on its own platform, Grammarly travels with you to places like Google Docs, email, or your Notes app to help improve your writing. Superhuman will use that superhighway to bring a huge new range of productivity tools to wherever you’re working.

In shedding the Grammarly name, Mehrota says:

“The trouble with the name ‘Grammarly’ is, like many names, its strength is its biggest weakness: it’s so precise,” Mehrotra says. “People’s expectations of what Grammarly can do for them are the reason it’s so popular. You need very little pitch for what it does, because the name explains the whole thing … As we went and looked at all the other things we wanted to be able to do for you, people scratch their heads a bit [saying], ‘Wait, I don’t really perceive Grammarly that way.’”

The company tapped branding agency Smith & Diction, the firm behind Perplexity’s brand identity.

Grammarly began briefing the Smith & Diction team on the rebrand in early 2025, but the company didn’t officially select its new name until late June, when the Superhuman acquisition was completed. For Chara and Mike Smith, the couple behind Smith & Diction, that meant there were only about three months to fully realize Superhuman’s branding.

Ouch, just three months for a complete rebrand. Ambitious indeed, but they hit a homerun with the icon, an arrow cursor which also morphs into a human with a cape, lovingly called “Hero.”

In their case study writeup, one of the Smiths says:

I was working on logo concepts and I was just drawing the basic shapes, you know the ones: triangles, circles, squares, octagons, etc., to see if I could get a story to fall out of any of them. Then I drew this arrow and was like hmm, that kinda looks like a cursor, oh wow it also kinda looks like a cape. I wonder if I put a dot on top of tha…OH MY GOD IT’S A SUPERHERO.

Check out the full case study for example visuals from the rebrand and some behind-the-scenes sketches.

What I’m Consuming

Why the Zune never killed the iPod. Launched in 2006 as a competitor to Apple’s iPod, Microsoft’s Zune spectacularly failed. Although initially marketed as an innovative device offering features like Wi-Fi and music sharing, the Zune struggled with software issues, limited compatibility, and an ineffective music ecosystem, ultimately failing to capture significant market share. The video reflects on Microsoft’s historical struggles with timing and execution in consumer electronics and the lessons learned from the Zune’s experience. (David Pierce / The Verge)

State of AI Report 2025. In 2025, AI systems have advanced significantly in reasoning capabilities, with major companies like OpenAI, Google, and China’s DeepSeek competing to develop models that can perform complex tasks and plan effectively. The commercial landscape for AI has expanded, with 44% of U.S. businesses utilizing AI tools and companies generating nearly $20 billion annually, marking the beginning of an industrial era characterized by massive data centers. Concurrently, the geopolitical dynamics of AI are evolving, with the U.S. adopting a national security-focused AI strategy, while China enhances its domestic AI ecosystem, and the conversation around AI safety shifts towards practical, tangible challenges rather than existential risks. (Nathan Benaich / Air Street Capital)

The Ezra Klein Show. The New York Times opinion columnist recently published a series of podcast episodes in which he explores how the Democrats have lost America and how to win the country back. But there are structural and foundational obstacles in the way.

• Trump Is Building the Blue Scare. A “Blue Scare” is rapidly weaponizing state and employer power to punish an amorphous left, echoing the Red Scare’s purges but faster, less restrained, and fueled by vengeance.

• Jon Favreau on Where the Democrats Went Right. Democrats finally picked a fight they can win on health care, but the deeper test is whether they can turn this shutdown into a clear, confident story about cost of living and creeping authoritarianism—without losing the midterms’ middle.

• How the Democratic Brand Turned Radioactive in Rural America. A rural-urban divide we once mistook for culture war is, at its core, a story of economic abandonment, organizational drift, and affinity politics—now powering a rural coalition that treats blue cities as enemy territory and leaves Democrats facing a brand problem they can’t fix with policy alone.

• How Can Democrats Win Back the Working Class? Democrats keep saying they’re the party of workers, but Jared Abbott’s research shows a stark “Democratic penalty”: working‑class voters now respond to plain‑spoken, economically populist candidates who break with party branding, sometimes even as independents—proof that tone, affect, and predistribution matter more than another promise of redistribution.

• This Is the Way You Beat Trump — and Trumpism. A durable Democratic majority won’t come from purity or a single grand rebrand—it’ll come from representing more kinds of voters in more kinds of places, building a coalition that prizes respect over preachiness and power over performance. I’ll leave you with Klein’s favorite line from a 1962 book by Bernard Crick, a political theorist and a democratic socialist: “Politics involves genuine relationships with people who are genuinely other people, not tasks set for our redemption or objects for our philanthropy.”