Huei-Hsin Wang at NN/g published a post about how to write better prompts for AI prompt-to-code tools.

When we asked AI-prototyping tools to generate a live-training profile page for NN/G course attendees, a detailed prompt yielded quality results resembling what a human designer created, whereas a vague prompt generated inconsistent and unpredictable outcomes across the board.

There’s a lot of detailing of what can often go wrong. Personally, I don’t need to read about what I experience daily, so the interesting bit for me is about two-thirds of the way into the article. Wang lists five strategies to employ to get better results.

• Visual intent: Name the style precisely—use concrete design vocabulary or frameworks instead of vague adjectives. Anchor prompts with recognizable patterns so the model locks onto the look and structure, not “clean/modern” fluff.
• Lightweight references: Drop in moodboards, screenshots, or system tokens to nudge aesthetics without pixel-pushing. Expect resemblance, not perfection; judge outcomes on hierarchy and clarity, not polish alone.
• Text-led visual analysis: Have AI describe a reference page’s layout and style in natural language, then distill those characteristics into a tighter prompt. Combine with an image when possible to reinforce direction.
• Mock data first: Provide realistic sample content or JSON so the layout respects information architecture. Content-driven prompts produce better grouping, hierarchy, and actionable UI than filler lorem ipsum.
• Code snippets for precision: Attach component or layout code from your system or open-source libraries to reduce ambiguity. It’s the most exact context, but watch length; use selectively to frame structure.

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.

This is a fascinating watch. Ryo Lu, Head of Design at Cursor builds a retro Mac calculator using Cursor agents while being interviewed. Lu’s personal website is an homage to Mac OX X, complete with Aqua-style UI elements. He runs multiple local background agents without stepping on each other, fixes bugs live, and themes UI to match system styles so it feels designed—not “purple AI slop,” as he calls it.

Lu, as interview by Peter Yang, on how engineers and designers work together at Cursor (lightly edited for clarity):

So at Cursor, the roles between designers, PM, and engineers are really muddy. We kind of do the part [that is] our unique strength. We use the agent to tie everything. And when we need help, we can assemble people together to work on the thing.

Maybe some of [us] focus more on the visuals or interactions. Some focus more on the infrastructure side of things, where you design really robust architecture to scale the thing. So yeah, there is a lot less separation between roles and teams or even tools that we use. So for doing designs…we will maybe just prototype in Cursor, because that lets us really interact with the live states of the app. It just feels a lot more real than some pictures in Figma.

And surprisingly, they don’t have official product managers at Cursor. Yang asks, “Did you actually actually hire a PM because last time I talked to Lee [Robinson] there was like no PMs.”

Lu again, and edited lightly for clarity:

So we did not hire a PM yet, but we do have an engineer who used to be a founder. He took a lot more of the PM-y side of the job, and then became the first PM of the company. But I would still say a lot of the PM jobs are kind of spread across the builders in the team.

That mostly makes sense because it’s engineers building tools for engineers. You are your audience, which is rare.

It’s always interesting for me to read how other designers use AI to vibe code their projects. I think using Figma Make to conjure a prototype is one thing, but vibe coding something in production is entirely different. Personally, I’ve been through it a couple of times that I’ve already detailed here and here.

Anton Sten recently wrote about his process. Like me, he starts in Figma:

This might be the most important part: I don’t start by talking to AI. I start in Figma.

I know Figma. I can move fast there. So I sketch out the scaffolding first—general theme, grids, typography, color. Maybe one or two pages. Nothing polished, just enough to know what I’m building.

Why does this matter? Because AI will happily design the wrong thing for you. If you open Claude Code with a vague prompt and no direction, you’ll get something—but it probably won’t be what you needed. AI is a builder, not an architect. You still have to be the architect.

I appreciate Sten’s conclusion to not let the AI do all of it for you, echoing Dr. Maya Ackerman’s sentiment of humble creative machines:

But—and this is important—you still need design thinking and systems thinking. AI handles the syntax, but you need to know what you’re building, why you’re building it, and how the pieces fit together. The hard part was never the code. The hard part is the decisions.

Economics PhD student Prashant Garg performed a fascinating analysis of Bob Dylan’s lyrics from 1962 to 2012 using AI. He detailed his project in Aeon:

So I fed Dylan’s official discography from 1962 to 2012 into a large language model (LLM), building a network of the concepts and connections in his songs. The model combed through each lyric, extracting pairs of related ideas or images. For example, it might detect a relationship between ‘wind’ and ‘answer’ in ‘Blowin’ in the Wind’ (1962), or between ‘joker’ and ‘thief’ in ‘All Along the Watchtower’ (1967). By assembling these relationships, we can construct a network of how Dylan’s key words and motifs braid together across his songs.

The resulting dataset is visualized in a series of node graphs and bar charts. What’s interesting is that AI is able to see Dylan’s work through a new lens, something that prior scholarship may have missed.

…Yet, when used as a lens rather than an oracle, the same models can jolt even seasoned critics out of interpretive ruts and reveal themes they might have missed. Far from reducing Dylan to numbers, this approach highlights how intentionally intricate his songwriting is: a restless mind returning to certain images again and again, recombining them in ever-new mosaics. In short, AI lets us test the folklore around Dylan, separating the theories that data confirm from those they quietly refute.

T-shaped, M-shaped, and now Σ-shaped designers?! Feels like a personality quiz or something. Or maybe designers are overanalyzing as usual.

Here’s Darren Yeo telling us what it means:

The Σ-shape defines the new standard for AI expertise: not deep skills, but deep synthesis. This integrator manages the sum of complex systems (Σ) by orchestrating the continuous, iterative feedback loops (σ), ensuring system outputs align with product outcomes and ethical constraints.

Whether you subscribe to the Three Lens framework as proposed by Oliver West, or this sigma-shaped one being proposed by Darren Yeo, just be yourself and don’t bring it up in interviews.

Hey designer, how are you? What is distracting you? Who are you having trouble working with?

Those are a couple of the questions designer Nikita Samutin and UX researcher Elizaveta Demchenko asked 340 product designers in a survey and in 10 interviews. They published their findings in a report called “State of Product Design: An Honest Conversation About the Profession.”

When I look at the calendars of the designers on my team, I see loads of meetings scheduled. So it’s no surprise to me that 64% of respondents said that switching between tasks distracted them. “Multitasking and unpredictable communication are among the main causes of distraction and stress for product designers,” the researchers wrote.

The most interesting to me, are the results in the section, “How Designers See Their Role.” Sixty-percent of respondents want to develop leadership skills and 47% want to improve presenting ideas.

For many, “leadership” doesn’t mean managing people—it means scaling influence: shaping strategy, persuading stakeholders, and leading high-impact projects. In other words, having a stronger voice in what gets built and why.

It’s telling because I don’t see pixel-pushing in the responses. And that’s a good thing in the age of AI.

Speaking of which, 77% of designers aren’t afraid that AI may replace them. “Nearly half of respondents (49%) say AI has already influenced their work, and many are actively integrating new tools into their processes. This reflects the state of things in early 2025.”

I’m sure that number would be bigger if the survey were conducted today.

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.

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.

This episode of Design of AI with 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 that can replace jobs—“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 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.

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.

When Figma acquired Weavy last month, I wrote a little bit about node-based UIs and ComfyUI. Looks like Adobe has been exploring this user interface paradigm as well.

Daniel John writes in Creative Bloq:

Project Graph is capable of turning complex workflows into user-friendly UIs (or ‘capsules’), and can access tools from across the Creative Cloud suite, including Photoshop, Illustrator and Premiere Pro – making it a potentially game-changing tool for creative pros.

But it isn’t just Adobe’s own tools that Project Graph is able to tap into. It also has access to the multitude of third party AI models Adobe recently announced partnerships with, including those made by Google, OpenAI and many more.

These tools can be used to build a node-based workflow, which can then be packaged into a streamlined tool with a deceptively simple interface.

And from Adobe’s blog post about Project Graph:

Project Graph is a new creative system that gives artists and designers real control and customization over their workflows at scale. It blends the best AI models with the capabilities of Adobe’s creative tools, such as Photoshop, inside a visual, node-based editor so you can design, explore, and refine ideas in a way that feels tactile and expressive, while still supporting the precision and reliability creative pros expect.

I’ve been playing around with ComfyUI a lot recently (more about this in a future post), so I’m very excited to see how this kind of UI can fit into Adobe’s products.

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.

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.

I’ve been playing with my systems in the past month—switching browsers, notetaking apps, and RSS feed readers. If I’m being honest, it’s causing me anxiety because I feel unmoored. My systems aren’t familiar enough to let me be efficient.

One thing that has stayed relatively stable is my LLM app—well, two of them. ChatGPT for everyday and Claude for coding and writing.

Christina Wodtke, writing on her blog:

The most useful model might not win.

What wins is the model that people don’t want to leave. The one that feels like home. The one where switching would mean losing something—not just access to features, but fluency, comfort, all those intangible things that make a tool feel like yours.

Amazon figured this out with Prime. Apple figured it out with the ecosystem. Salesforce figured it out by making itself so embedded in enterprise workflows that ripping it out would require an act of God.

AI companies are still acting like this is a pure technology competition. It’s not. It’s a competition to become essential—and staying power comes from experience, not raw capability.

Your moat isn’t your model. Your moat is whether users feel at home.

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.

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.

For building with AI, Litt advocates “coding like a surgeon”: stay in the loop and use agents for prep and grunt work.

How do we think of AI as a way to leverage our time better? [So we can] stay connected to the work and [do] it ourselves by having prep work done for us. Having tools in the moment helping us do it so that we can really focus on the stuff we love to do, and do less of everything else. And that’s how I’m trying to use coding agents for my core work that I care about today. Which is when I show up, sit down at my desk in the morning and work on a feature, I want to be prepped with a brief on all the code I’m going to be touching today, how it works, what the traps are. Maybe I’ll see a draft that the AI did for me overnight, sketching out how the coding could go. Maybe some ideas for me.

In other words, like an assistant who works overnight. And yeah, this could apply to design as well.

He told me his CEO - who’s never written a line of code - was running their company from an AI code editor.

I almost fell out of my chair.

OF COURSE. WHY HAD I NOT THOUGHT OF THAT.

I’ve since gotten rid of almost all of my productivity tools.

ChatGPT, Notion, Todoist, Airtable, Google Keep, Perplexity, my CRM. All gone.

That’s the lede for a piece by Derek Larson on running everything from Claude Code. I’ve covered how Claude Code is pretty brilliant and there are dozens more use cases than just coding.

But getting rid of everything and using just text files and the terminal window? Seems extreme.

Larson uses a skill in Claude Code called “/weekly” to do a weekly review.

1. Claude looks at every file change since last week
2. Claude evaluates the state of projects, tasks, and the roadmap
3. We have a conversation to dig deeper, and make decisions
4. Claude generates a document summarizing the week and plan we agreed on

Then Claude finds items he’s missed or procrastinating on, and “creates a space to dump everything” on his mind.

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.

Francesca Bria and her collaborators analyzed open-source datasets of “over 250 actors, thousands of verified connections, and $45 billion in documented financial flows” to come up with a single-page website visually showing such connections.

J.D. Vance, propelled to the vice-presidency by $15 million from Peter Thiel, became the face of tech-right governance. Behind him, Thiel’s network moved into the machinery of the state.

Under the banner of “patriotic tech”, this new bloc is building the infrastructure of control—clouds, AI, finance, drones, satellites—an integrated system we call the Authoritarian Stack. It is faster, ideological, and fully privatized: a regime where corporate boards, not public law, set the rules.

Our investigation shows how these firms now operate as state-like powers—writing the rules, winning the tenders, and exporting their model to Europe, where it poses a direct challenge to democratic governance.

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.