Every article I share on this blog starts the same way: in my RSS reader. I use Inoreader to follow about a hundred feeds—design blogs, tech publications, and independent newsletters. Every morning I scroll through what’s new, mark what’s interesting, and the best stuff eventually becomes a link post here. It’s not a fancy workflow. It’s an RSS reader and a notes app. But it works because the format works.

This is a 2023 article, but I’m fascinated by it because Google Reader was so influential in my life. David Pierce, writing for The Verge, chronicles how Google Reader came to be and why Google killed it.

Chris Wetherell, who built the first prototype, wasn’t thinking about an RSS reader. He was thinking about a universal information layer:

“I drew a big circle on the whiteboard,” he recalls. “And I said, ‘This is information.’ And then I drew spokes off of it, saying, ‘These are videos. This is news. This is this and that.’” He told the iGoogle team that the future of information might be to turn everything into a feed and build a way to aggregate those feeds.

Jason Shellen, the product manager, saw the same thing:

“We were trying to avoid saying ‘feed reader,’” Shellen says, “or reading at all. Because I think we built a social product.”

Google couldn’t see it. Reader had 30 million users, many of them daily, but that was a rounding error by Google standards. Pierce captures the absurdity well:

Almost nothing ever hits Google scale, which is why Google kills almost everything.

So Google poured its resources into Google Plus instead. That product was dead within months of launch. Reader, the thing they killed to make room for it, had been a working social network the whole time. Jenna Bilotta, a designer on the team:

“They could have taken the resources that were allocated for Google Plus, invested them in Reader, and turned Reader into the amazing social network that it was starting to be.”

What gets me is that the vision Wetherell drew on that whiteboard—a single place to follow everything you care about, organized by your taste, shared with people you trust, and non-algorithmic—still doesn’t fully exist. RSS readers are the closest thing we have, and they’re good enough that I’ve built my entire reading and writing practice around one. But the curation layer Wetherell imagined is still unfinished.

Buying something used to be the end of the story. You liked it, you bought it, you used it. Now buying is the beginning. The real work starts when you film the unboxing. At least according to Lucinda Bounsall. Writing for Print Magazine, she argues that platforms have turned everyday life into a continuous production set:

Platforms have normalised a way of living where everyday life is quietly organised around being watchable. Bedrooms become sets, bathrooms become studios, private routines become content addressed to an imagined audience. The effect can feel faintly dystopian; a distributed Truman Show, in which people are advertising products, habits, and lifestyles to unseen cameras, without ever being cast or paid.

Bounsall traces an inversion that anyone who’s worked on a brand should pay attention to. As brands become more abstract and conceptual, the labor of making them mean something falls on the individual:

As brands move up the pyramid, abstracting themselves into values, worlds, and cultural posture, individuals are pushed in the opposite direction, down into labour. The work of meaning-making, distribution, and identity signalling increasingly falls on the person rather than the company. Brands become lighter, more conceptual, more removed; people become the infrastructure through which those brands are activated, circulated, and made legible.

In a way, Bounsall describes what brand has always been at its core—how customers perceive a company or product. It’s always been in the hands of the people.

In her closing, Bounsall sees people starting to pull back into spaces that can’t be monetized:

This doesn’t signal an end to the brandification of the self, but a growing desire to reclaim parts of the self that don’t need to be performed, optimised, or read. Not everything is meant to circulate. And increasingly, that is kind of the point.

That last line pairs with the Dan Abramov piece I linked previously. Abramov argues for an architecture where your data outlives the apps you create it in. Bounsall is making the cultural case for the same impulse: some things should belong to you and not be put into circulation just because a platform makes it easy.

Designers know this feeling. Your work lives inside whatever tool made it—Figma, Miro, Framer—and when that tool changes its pricing, gets acquired, or shuts down, your files go with it. We’ve now accepted this as normal.

Dan Abramov argues it shouldn’t be. In a long post about the AT Protocol (the technology behind Bluesky), he starts with how files used to work on personal computers:

The files paradigm captures a real-world intuition about tools: what we make with a tool does not belong to the tool. A manuscript doesn’t stay inside the typewriter, a photo doesn’t stay inside the camera, and a song doesn’t stay in the microphone.

He takes that intuition and applies it to social computing. What if your posts, likes, and follows were files you owned instead of data locked inside Instagram or Twitter? Abramov calls this a “social filesystem” and walks through how the AT Protocol makes it real, from records and collections to identity and links, all building toward one idea:

Our memories, our thoughts, our designs should outlive the software we used to create them. An app-agnostic storage (the filesystem) enforces this separation.

That word “designs” jumped out at me. Abramov is talking about social data, but the same logic applies to creative work. The inversion he describes, where apps react to your data rather than owning it, is the opposite of how most design tools work today:

In this paradigm, apps are reactive to files. Every app’s database mostly becomes derived data—an app-specific cached materialized view of everybody’s folders.

One of the reasons I write content here on this blog as opposed to writing in a social network or even Substack—though my newsletter is on Substack, humans aren’t perfect—is because I want the control and ownership Abramov brings up.

The whole post is worth reading. Abramov makes the AT Protocol’s architecture feel inevitable rather than complicated, and his closing line is the one I keep thinking about: “An everything app tries to do everything. An everything ecosystem lets everything get done.”

Back in September, when Trump announced America by Design and appointed Joe Gebbia as Chief Design Officer, I wrote that it was “yet another illustration of this administration’s incompetence.” The executive order came months after DOGE gutted 18F and the US Digital Service, the agencies that had spent a decade building the expertise Gebbia now claims to be inventing.

Mark Wilson, writing for Fast Company, spoke to a dozen government designers about how Gebbia’s tenure has played out. When Wilson asked Gebbia about USDS and 18F—whether he thought these groups were overrated and needed to be rebuilt—here’s what he said:

“Without knowing too much about the groups you mentioned, I do know that the air cover and the urgency around design is in a place it’s [never] been before.”

He doesn’t know much about them. The agencies his administration destroyed. The hundreds of designers recruited from Google, Amazon, and Facebook who fixed healthcare.gov and built the COVID test ordering system. He doesn’t know much about them.

Mikey Dickerson, who founded USDS, on the opportunity Gebbia inherited:

“He’s inheriting the blank check kind of environment… [so] according to the laws of physics, he should be able to get a lot done. But if the things that he’s allowed to do, or the things that he wants to do, are harmful, then he’ll be able to do a lot of harm in a really short amount of time.”

And what has Gebbia done with that blank check? He’s built promotional websites for Trump initiatives: trumpaccounts.gov, trumpcard.gov, trumprx.com. Paula Scher of Pentagram looked at the work:

“The gold card’s embarrassing. The typeface is hackneyed.”

But Scher’s real critique goes beyond aesthetics.

“You can’t talk about people losing their Medicare and have a slick website,” says Paula Scher. “It just doesn’t go.”

That’s the contradiction at the center of America by Design. You can’t strip food stamps, gut healthcare subsidies, and purge the word “disability” from government sites, then turn around and promise to make government services “delightful.” The design isn’t the problem. The policy is.

Scher puts it plainly:

“[Trump] wants to make it look like a business. It’s not a business. The government is a place that creates laws and programs for society—it’s not selling shit.”

Wilson’s piece is long and worth reading in full. There’s more on what USDS and 18F actually accomplished, and on the designers who watched their work get demolished by people who didn’t understand it.

The optimistic case for designers in an AI-driven world is that design becomes strategy—defining what to build, not just how it looks. But are designers actually making that shift?

Noam Segal and Lenny Rachitsky, writing for Lenny’s Newsletter, share results from a survey of 1,750 tech workers. The headline is that AI is “overdelivering”—55% say it exceeded expectations, and most report saving at least half a day per week. But the findings by role tell a different story for designers:

Designers are seeing the fewest benefits. Only 45% report a positive ROI (compared with 78% of founders), and 31% report that AI has fallen below expectations, triple the rate among 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. And product managers are building prototypes themselves:

Compare prototyping: PMs have it at #2 (19.8%), while designers have it at #4 (13.2%). AI is unlocking skills for PMs outside of their core work, whereas designers aren’t seeing the marginal improvement benefits from AI doing their core work.

The survey found that AI helps designers with work around design—research synthesis, copy, ideation—but visual design ranks #8 at just 3.3%. As Segal puts it:

AI is helping designers with everything around design, but pushing pixels remains stubbornly human.

This is the gap. The strategic future is available, but designers aren’t capturing it at the same rate as other roles. The question is why—and what to do about it.

The rise of micro apps describes what’s happening from the bottom up—regular people building their own tools instead of buying software. But there’s a top-down story too: the structural obsolescence of traditional software companies.

Doug O’Laughlin makes the case using a hardware analogy—the memory hierarchy. AI agents are fast, ephemeral memory (like DRAM), while traditional software companies need to become persistent storage (like NAND, or ROM if you’re old school like me). The implication:

Human-oriented consumption software will likely become obsolete. All horizontal software companies oriented at human-based consumption are obsolete.

That’s a bold claim. O’Laughlin goes further:

Faster workflows, better UIs, and smoother integrations will all become worthless, while persistent information, a la an API, will become extremely valuable.

As a designer, this is where I start paying close attention. The argument is that if AI agents become the primary consumers of software—not humans—then the entire discipline of UI design is in question. O’Laughlin names names:

Figma could be significantly disrupted if UIs, as a concept humans create for other humans, were to disappear.

I’m not ready to declare UIs dead. People still want direct manipulation, visual feedback, and the ability to see what they’re doing. But the shift O’Laughlin describes is real: software’s value is migrating from presentation to data. The interface becomes ephemeral—generated on the fly, tailored to the task—while the source of truth persists.

This is what I was getting at in my HyperCard essay: the tools we build tomorrow won’t look like the apps we buy today. They’ll be temporary, personal, and assembled by AI from underlying APIs and data. The SaaS companies that survive will be the ones who make their data accessible to agents, not the ones with the prettiest dashboards.

Almost a year ago, I linked to Lee Robinson’s essay “Personal Software” and later explored why we need a HyperCard for the AI era. The thesis: people would stop searching the App Store and start building what they need. Disposable tools for personal problems.

That future is arriving. Dominic-Madori Davis, writing for TechCrunch, documents the trend:

It is a new era of app creation that is sometimes called micro apps, personal apps, or fleeting apps because they are intended to be used only by the creator (or the creator plus a select few other people) and only for as long as the creator wants to keep the app. They are not intended for wide distribution or sale.

What I find compelling here is the word “fleeting.” We’ve been conditioned to think of software as permanent infrastructure—something you buy, maintain, and eventually migrate away from. But these micro apps are disposable by design. One founder built a gaming app for his family to play over the holidays, then shut it down when vacation ended. That’s not a failed product. That’s software that did exactly what it needed to do.

Howard University professor Legand L. Burge III frames it well:

It’s similar to how trends on social media appear and then fade away. But now, [it’s] software itself.

The examples in the piece range from practical (an allergy tracker, a parking ticket auto-payer) to whimsical (a “vice tracker” for monitoring weekend hookah consumption). But the one that stuck with me was the software engineer who built his friend a heart palpitation logger so she could show her doctor her symptoms. That’s software as a favor. Software as care.

Christina Melas-Kyriazi from Bain Capital Ventures offers what I think is the most useful framing:

It’s really going to fill the gap between the spreadsheet and a full-fledged product.

This is exactly right. For years, spreadsheets have been the place where non-developers build their own tools—janky, functional, held together with VLOOKUP formulas and conditional formatting. Micro apps are the evolution of that impulse, but with real interfaces and actual logic.

The quality concerns are real—bugs, security flaws, apps that only their creator can debug. But for personal tools that handle personal problems, “good enough for one” is genuinely good enough.

Claude Code is having a moment. Anthropic’s agentic coding tool has gone viral over the past few weeks, with engineers and non-engineers alike discovering what it feels like to hand real work over to an AI and watch it execute autonomously. The popular tech podcast Hard Fork has already had two segments on it in the last two weeks. In the first, hosts Kevin Roose and Casey Newton share their Claude Code projects. And in the second, they highlight some from their listeners. (Alas, my Severance fan project did not make the cut.)

I’ve been using Cursor and Claude Code to build and rebuild this site for over a year now, so when I read this piece and see coders describing their experience with it, I understand the feeling.

Bradley Olson (gift link), writing for the Wall Street Journal:

Some described a feeling of awe followed by sadness at the realization that the program could easily replicate expertise they had built up over an entire career.

“It’s amazing, and it’s also scary,” said Andrew Duca, chief executive of Awaken Tax, a cryptocurrency tax platform. Duca has been coding since he was in middle school. “I spent my whole life developing this skill, and it’s literally one-shotted by Claude Code.”

Duca decided not to hire the engineers he’d been planning to bring on. He thinks Claude makes him five times more productive.

The productivity numbers throughout the piece are striking:

Malte Ubl is chief technology officer at Vercel, which helps develop and host websites and apps for users of Claude Code and other such tools. He said he used the tool to finish a complex project in a week that would’ve taken him about a year without AI. Ubl spent 10 hours a day on his vacation building new software and said each run gave him an endorphin rush akin to playing a Vegas slot machine.

But what caught my attention is what people are using it for beyond code—analyzing MRI data, recovering wedding photos from corrupted drives, monitoring tomato plants with a webcam. Olson again:

Unlike most app- or web-bound chatbots now in wide use, it can operate autonomously, with broad access to user files, a web browser and other applications. While technologists have predicted a coming era of AI “agents” capable of doing just about anything for humans, that future has been slow to develop. Using Claude Code was the first time many users interacted with this kind of AI, offering an inkling of what may be in store.

Anthropic took notice of course and launched a beta of Cowork last week.

Instead of the MS-DOS-like “command line” interface that the core app has, Cowork displays a more friendly, graphical user interface. They built the product in about 10 days—using Claude Code.

The closing question is the right one:

“The bigger story here is going to be when this goes beyond software engineering,” said David Hsu, chief executive of Retool, a business-AI startup. Software engineers make up a tiny fraction of the U.S. labor force. “How far does it go?”

Replace “software engineering” with “design” and you have the question I’m exploring this week.

My wife is an obesity medicine and women’s health specialist, so she’s been in my ear talking about ultraprocessed foods for years. That’s why the processed food analogy for AI-generated software resonates. We industrialized agriculture and got abundance, yes—but also obesity, diabetes, and 318 million people still experiencing acute hunger. The problem was never production capacity.

Chris Loy applies this lens to where software is heading:

Industrial systems reliably create economic pressure toward excess, low quality goods. This is not because producers are careless, but because once production is cheap enough, junk is what maximises volume, margin, and reach. The result is not abundance of the best things, but overproduction of the most consumable ones.

Loy introduces the term “disposable software”—software created with no expectation of ownership, maintenance, or long-term understanding. Vibe-coded apps. AI slop. Whatever you want to call it, the economics are different: easy reproducibility means each output has less value, which means volume becomes the only game. Just look in the App Store for any popular category such as todo lists, notetakers, and word puzzles. Or look in r/SaaS and notice the glut of single people building and selling their own products.

Loy goes on to compare this movement with mass-produced fashion as well:

For example, prior to industrialisation, clothing was largely produced by specialised artisans, often coordinated through guilds and manual labour, with resources gathered locally, and the expertise for creating durable fabrics accumulated over years, and frequently passed down in family lines. Industrialisation changed that completely, with raw materials being shipped intercontinentally, fabrics mass produced in factories, clothes assembled by machinery, all leading to today’s world of fast, disposable, exploitative fashion.

Disposable fashion leads to vast overproduction, with estimates that 20–40% (up to 30–60 billion pieces) go unsold. There’s a waste of people’s time, tokens, electricity, and ultimately consumer dollars that AI enables.

The silver lining that Loy observes is in innovation. Entirely human-written code isn’t the answer. It’s doing the necessary research and development to innovate. My take is that’s exactly where designers need to be sitting.

Yesterday, Anthropic launched Cowork, a research preview that is essentially Claude Code but for non-coders.

From the blog announcement:

How is using Cowork different from a regular conversation? In Cowork, you give Claude access to a folder of your choosing on your computer. Claude can then read, edit, or create files in that folder. It can, for example, re-organize your downloads by sorting and renaming each file, create a new spreadsheet with a list of expenses from a pile of screenshots, or produce a first draft of a report from your scattered notes.

In Cowork, Claude completes work like this with much more agency than you’d see in a regular conversation. Once you’ve set it a task, Claude will make a plan and steadily complete it, while looping you in on what it’s up to. If you’ve used Claude Code, this will feel familiar—Cowork is built on the very same foundations. This means Cowork can take on many of the same tasks that Claude Code can handle, but in a more approachable form for non-coding tasks.

Apparently, Cowork was built very quickly using—naturally—Claude Code. Michael Nuñez in VentureBeat:

…according to company insiders, the team built the entire feature in approximately a week and a half, largely using Claude Code itself.

Alas, this is only available to Claude Max subscribers ($100–200 per month). I will need to check it out when it’s more widely available.

The Whole Earth Catalog, published by Stewart Brand several times a year between 1968 and 1972 (and occasionally until 1998), was the internet before the internet existed. It curated tools, books, and resources for self-education and DIY living, embodying an ethos of access to information that would later define the early web. Steve Jobs famously called it “one of the bibles of my generation,” and for good reason—its approach to democratizing knowledge and celebrating user agency directly influenced the philosophy of personal computing and the participatory culture we associate with the web’s early days.

Curated by Barry Threw and collaborators, the Whole Earth Index is a near-complete archive of the issues of the Whole Earth Catalog.

Here lies a nearly-complete archive of Whole Earth publications, a series of journals and magazines descended from the Whole Earth Catalog, published by Stewart Brand and the POINT Foundation between 1968 and 2002. They are made available here for scholarship, education, and research purposes.

The info page also includes a quote from Stewart Brand:

“Dateline Oct 2023, Exactly 55 years ago, in 1968, the Whole Earth Catalog first came to life. Thanks to the work of an ongoing community of people, it prospered in various forms for 32 years—sundry editions of the Whole Earth Catalog, CoEvolution Quarterly, The WELL, the Whole Earth Software Catalog, Whole Earth Review, etc. Their impact in the world was considerable and sustained. Hundreds of people made that happen—staff, editors, major contributors, board members, funders, WELL conference hosts, etc. Meet them here.” —Stewart Brand

Brand’s mention of The WELL is particularly relevant here—he founded that pioneering online community in 1985 as a digital extension of the Whole Earth ethos, creating one of the internet’s first thriving social networks.

On the heels of OpenAI’s report “The state of enterprise AI,” Anthropic published a blog post detailing research about how AI is being used by the employees building AI. The researchers surveyed 132 engineers and researchers, conducted 53 interviews, and looked at Claude usage data.

Our research reveals a workplace facing significant transformations: Engineers are getting a lot more done, becoming more “full-stack” (able to succeed at tasks beyond their normal expertise), accelerating their learning and iteration speed, and tackling previously-neglected tasks. This expansion in breadth also has people wondering about the trade-offs—some worry that this could mean losing deeper technical competence, or becoming less able to effectively supervise Claude’s outputs, while others embrace the opportunity to think more expansively and at a higher level. Some found that more AI collaboration meant they collaborated less with colleagues; some wondered if they might eventually automate themselves out of a job.

The post highlights several interesting patterns.

• Employees say Claude now touches about 60% of their work and boosts output by roughly 50%.
• Employees say that 27% of AI‑assisted tasks is work that wouldn’t have happened otherwise—like papercut fixes, tooling, and exploratory prototypes.
• Engineers increasingly use it for new feature implementation and even design/planning.

Perhaps most provocative is career trajectory. Many engineers describe becoming managers of AI agents, taking accountability for fleets of instances and spending more time reviewing than writing net‑new code. Short‑term optimism meets long‑term uncertainty: productivity is up, ambition expands, but the profession’s future shape—levels of abstraction, required skills, and pathways for growth—remains unsettled. See also my series on the design talent crisis.

A new documentary called The Age of Audio traces the history and impact of podcasting, exploring the resurgence of audio storytelling in the 21st century. In a clip from the doc in the form of a short, Ben Hammersley tells the story of how he coined the term “podcast.”

I’m Ben Hammersley, and I do many things, but mostly I’m the person who invented the word podcast. And I am very sorry.

I can tell you the story. This was in 2004, and I was a writer for the Guardian newspaper in the UK. And at the time, the newspaper was paper-centric, which meant that all of the deadlines were for the print presses to run. And I’d written this article about this sort of emerging idea of downloadable audio content that was automatically downloaded because of an RSS feed.

I submitted the article on time, but then I got a phone call from my editor about 15 minutes before the presses were due to roll saying, “Hey, that piece is about a sentence short for the shape of the page. We don’t have time to move the page around. Can you just write us another sentence?”

And so I just made up a sentence which says something like, “But what do we call this phenomenon?” And then I made up some silly words. It went out, it went into the article, didn’t think any more of it.

And then about six months later or so, I got an email from the Oxford American Dictionary saying, “Hey, where did you get that word from that was in the article you wrote? It seems to be the first citation of the word ‘podcast.’ Now here we are almost 20 years later, and it became part of the discourse.” I’m totally fine with it now.

(h/t Jason Kottke / Kottke.org)

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.

For those of you who might not know, Rei Inamoto is a designer who has helped shape some of the most memorable marketing sites and brand campaigns of the last 20+ years. He put digital agency AKQA on the map and has been named as one of “the Top 25 Most Creative People in Advertising” in Forbes Magazine.

Inamoto has made some predictions for 2026:

1. TV advertising strikes back: Nike releases an epic film ad around the World Cup. Along with its strong product line-up, the stock bounces back, but not all the way.
2. Relevance > Reach: ON Running tops $5B in market cap; Lexus crosses 1M global sales.
3. The new era of e-commerce: Direct user traffic to e‑commerce sites declines 5–10%, while traffic driven by AI agents increases 50%+.
4. New form factor of AI: OpenAI announces its first AI device—a voice-powered ring, bracelet, or microphone.

Bracelet?! I hadn’t thought of that! Back in May, when OpenAI bought Jony Ive’s io, I predicted it will be an earbud. A ring or bracelet is interesting. Others have speculated it might be a pendant.

Anand Majmudar creates a scenario inspired by “AI 2027”, but focused on robotics.

I created Android Dreams because I want the good outcomes for the integration of automation into society, which requires knowing how it will be integrated in the likely scenario. Future prediction is about fitting the function of the world accurately, and the premise of Android Dreams is that my world model in this domain is at least more accurate than on average. In forming an accurate model of the future, I’ve talked to hundreds of researchers, founders, and operators at the frontier of robotics as my own data. I’m grateful to my mentors who’ve taught me along the way.

The scariest scenes from “AI 2027” are when the AIs start manufacturing and proliferating robots. For example, from the 2028 section:

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

So I’m glad for Majmudar’s thought experiment.

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.

From the Sep 2025 Harvard University paper, “Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data.” (link)

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.

Hard to believe that the very first fully computer animated feature film came out 30 years ago. To say that Toy Story was groundbreaking would be an understatement. If you look at the animated feature landscape today, 100% is computer-generated.

In this re-found interview with Steve Jobs exactly a year after the movie premiered in theaters, Jobs talks about a few things, notably how different Silicon Valley and Hollywood were—and still are.

From the Steve Jobs Archive:

In this footage, Steve reveals the long game behind Pixar’s seeming overnight success. With striking clarity, he explains how its business model gives artists and engineers a stake in their creations, and he reflects on what Disney’s hard-won wisdom taught him about focus and discipline. He also talks about the challenge of leading a team so talented that it inverts the usual hierarchy, the incentives that inspire people to stay with the company, and the deeper purpose that unites them all: to tell stories that last and put something of enduring value into the culture.  

And Jobs in his own words:

Well, in this blending of a Hollywood  culture and a Silicon Valley culture, one of the things that we encountered was  that the Hollywood culture and the Silicon Valley culture each used different models of  employee retention. Hollywood uses the stick, which is the contract, and Silicon Valley  uses the carrot, which is the stock option. And we examined both of those in really pretty  great detail, both economically, but also psychologically and culture wise, what kind of  culture do you end up with. And while there’s a lot of reasons to want to lock down your  employees for the duration of a film because, if somebody leaves, you’re at risk, those  same dangers exist in Silicon Valley. During an engineering project, you don’t want to lose people, and yet, they managed to evolve another system than contracts. And we preferred the Silicon Valley model in this case, which basically gives people stock in the company so that we all have the same goal, which is to create shareholder value. But also, it makes us constantly worry about making Pixar the greatest company we can  so that nobody would ever want to leave. 

Pavel Bukengolts writes a piece for UX Magazine that reiterates what I’ve been covering here: our general shift to AI means that human judgement and adaptability are more important than ever.

Before getting to the meat of the issue, Bukengolts highlights the talent crisis that is our own making:

The outcome is a broken pipeline. If graduates cannot land their first jobs, they cannot build the experience needed for the next stage. A decade from now, organizations may face not just a shortage of junior workers, but a shortage of mid-level professionals who never had a chance to develop.

If rote repetitive tasks are being automated by AI and junior staffers aren’t needed for those tasks, then what skills are still valuable? Further on, he answers that question:

Centuries ago, in Athens, Alexandria, or Oxford, education focused on rhetoric, logic, and philosophy. These were not academic luxuries but survival skills for navigating complexity and persuasion. Ironically, they are once again becoming the most durable protection in an age of automation.

Some of these skills include:

• Logic: Evaluating arguments and identifying flawed reasoning—essential when AI generates plausible but incorrect conclusions.
• Rhetoric: Crafting persuasive narratives that create emotional connection and resonance beyond what algorithms can achieve.
• Philosophy and Ethics: Examining not just capability but responsibility, particularly around automation’s broader implications.
• Systems Thinking: Understanding interconnections and cascading effects that AI’s narrow outputs often miss.
• Writing: Communicating with precision to align stakeholders and drive better outcomes.
• Observation: Detecting subtle signals and anomalies that fall outside algorithmic training data.
• Debate: Refining thinking through intellectual challenge—a practice dating to ancient dialogue.
• History: Recognizing recurring patterns to avoid cyclical mistakes; AI enthusiasm echoes past technological revolutions.

I would say all of the above not only make a good designer but a good citizen of this planet.

In a heady, intelligent, and fascinating interview with Sarah Jeong from The Verge, Cory Doctorow—the famed internet activist—talks about how platforms have gotten worse over the years. Using Meta (Facebook) as an example, Doctorow explains their decline over time through a multi-stage process. Initially, it attracted users by promising not to spy on them and by showing them content from their friends, leveraging the difficulty of getting friends to switch platforms. Subsequently, Meta compromised user privacy by providing advertisers with surveillance data (aka ad tracking) and offered publishers traffic funnels, locking in business customers before ultimately degrading the experience for all users by filling feeds with paid content and pivoting to less desirable ventures like the Metaverse.

And publishers, [to get visibility on the platform,] they have to put the full text of their articles on Facebook now and no links back to their website.

Otherwise, they won’t be shown to anyone, much less their subscribers, and they’re now fully substitutive, right? And the only way they can monetize that is with Facebook’s rigged ad market and users find that the amount of stuff that they ask to see in their feed is dwindled to basically nothing, so that these voids can be filled with stuff people will pay to show them, and those people are getting ripped off. This is the equilibrium Mark Zuckerberg wants, right? Where all the available value has been withdrawn. But he has to contend with the fact that this is a very brittle equilibrium. The difference between, “I hate Facebook, but I can’t seem to stop using it,” and “I hate Facebook and I’m not going to use it anymore,” is so brittle that if you get a live stream mass shooting or a whistleblower or a privacy scandal like Cambridge Analytica, people will flee.