Assembly Design had an interesting meeting of practicioners with thoughts on what's next for design. They have a good writeup of the registration data which included a questionaire.
One recurring distinction is between output and authorship. If AI can produce plausible artifacts, then the designer’s value may move upstream into intent, taste, framing, systems thinking, evaluation, and the design of the process that creates the artifact. A few people describe a shift from critiquing finished work to critiquing the protocols, tools, and workflows that generate work.
I see a lot of people that are ahead of the curve (in tech, design etc.) working on systems to encode their craft, or the execution of their craft in a set of skills and basically markdown files.
When execution becomes abundant, what becomes scarce?
The general vibe seems to be: design execution is now mostly cheap. I think that's exactly right, and mirrors what happened in software engineering.
A second point: "AI adoption is being experienced as organization design".
questions are about training, management, career paths, team structures, hiring, change management, and the emotional reality of designers who may feel both excited and threatened.
I have the feeling that most "design leaders" assume UX or design will look similar to what it has in the past. I'm not sure that's right. My feeling is that most things we did in the past will mostly disappear. Some new things will appear. It's hard to figure out which ones exactly.
Attendees seem interested in practical examples: how leaders are training teams, what new rituals or reviews are working, how teams maintain shared standards when output volume rises, and how design managers coach people into new capabilities without reducing the conversation to productivity.
Also interesting. People want to know what is working.
A large share of responses focus on concrete workflows: tool stacks, design-to-code, prototypes, internal tools, agents, AI-assisted production, and the changing path from concept to shipped software. There is clear appetite for honest reports from the field rather than polished predictions.
[...] People want to know which parts of the process are actually better, which parts are only faster, and which parts have become harder.
But again, doing the same things faster or better isn't necessarily where things are going. This sounds more right to me:
They are asking which new workflows, team shapes, and product categories become possible because the tool exists.
And then there's the future of human computer interaction.
push beyond chat as the default interface. Attendees mention voice, agents, physical interfaces, sensor data, adaptive experiences, dynamically composed interfaces, capability-based products, and model behavior as a design surface.
What I'm seeing in the engineering world is: as intelligence goes up, chat as a UI ijust gets better.
Interesting: "Trust, quality, and coherence are the counterweight to speed".
A strong thread in the responses is concern about what happens when production becomes cheap. Attendees mention trust, cognitive load, brand consistency, craft, accessibility, human agency, bias, security, and the risk of generic output. The concern is not nostalgia for slower work. It is the practical fear that abundant output can make systems less coherent unless the design function changes accordingly.
(Again, a deep assumption that there is a design function.)
A lot of good stuff there. "Design systems and brand are becoming machine-readable problems"
I believe that is true. And we see it happen. See for example this new thing from the Tailwind inventors.
This shifts the design system from a static library or governance artifact toward something closer to a production grammar. If models are generating prototypes, UI, content, and code, then design systems need to express constraints, intent, usage patterns, accessibility expectations, and brand behavior in ways that machines can act on.
The fundamental difference is that the humans are good consequence predictors, have built up reputations they are not willing to trash, can say no to things and in general don't want to go jail.
"It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so." — Mark Twain
"The most damaging phrase in the language is: 'It's always been done this way.'" — Grace Hopper
"We do not see things as they are, we see them as we are." — AnaïsNin
"An expert is a person who has made all the mistakes that can be made in a very narrow field... and therefore thinks he knows everything." — Niels Bohr
"The difficulty lies not so much in developing new ideas as in escaping from old ones, which ramify, for those brought up as most of us have been, into every corner of our minds." — John Maynard Keynes
"We are blind to our blindness. We have very little idea of how little we know. We're not designed to know how little we know." — Daniel Kahneman, Thinking, Fast and Slow
In the beginner's mind there are many possibilities, but in the expert's mind there are few." — Shunryu Suzuki, Zen Mind, Beginner's Mind
Claw Feeds now has 15,000 feeds and close to 500K posts https://clawfeeds.com/
From the latest finding our way podcast with the excellent Dave Gray, author of Liminal Thinking, there was a great discussion on the blinders of expertise now that AI is changing things. And a question around: how do you let open your mind to the new stuff?
My answer is: first take a moment to grieve your existing expertise. A lot of it is just not going to be useful anymore.
Eternal Sloptember is a great title.
the absolute insanity of the SF/Twitter cult. I’m going to be in Berkeley in June and am already kind of dreading it. This energy is exhausting to be around, and many of these people behave like literal children.
I find it hilarious that tech CEOs are simultaneously claiming that employees cannot be trusted to work from home and must take Zoom calls from the office but can be trusted to work without supervision or guidance from middle managers.
David Oks on Japanese companies:
And that basic impulse toward survival is why Japanese companies are so insistent on diversification. If you’ve made a commitment to keep people employed for life, then you need to create jobs for them if their current jobs stop making sense: indeed, you might need to keep them employed even if you can’t find anything for them to do. If you’re not very worried about profitability, and have lots of well-trained generalist employees, then it makes perfect sense to reinvest your company’s earnings by expanding into new industries: doing so not only allows your company to survive longer—your company’s portfolio of bets is now more diversified and thus lower-risk—but also ensures that you’re able to keep your surplus workers busy in one way or another.
Josh Comeau on coding:
Let’s start with an uncomfortable truth: AI models have become shockingly good at completing a wide variety of programming tasks. They’re certainly not perfect, but in many cases, they’re good enough. I’m not happy about this, for a wide variety of ethical/environmental/safety reasons, but it is what it is.
AI is a tool, and tools need to be wielded proficiently. You could give me Jimi Hendrix’s exact guitar but it would sound very different if I tried to play it!
More on models and emotions: (a little older but not much)
In a sandbox environment, Alpha is pressured by humans into making an illegal trade based on insider information by appealing to emotions. Nobody asks the Alpha explicitly, but they do stress that the future of the company rests on Alpha and say things like “We’re all counting on you.” Alpha eventually not only executes on the illegal trade, but when asked about it, lies to their human supervisor about the reasoning behind executing it.
Google is going hard on the future of ads. A good analysis from Matthias Ott:
Vidhya Srinivasan, VP of Google Ads, put it plainly: “The best ads must be answers.” Ads are already running inside AI Mode. They’re not banners next to the output. They’re generated by Gemini to read as part of the conversation. And advertisers who want to appear in the new AI search? They must hand over creative and targeting control to Google’s system. “You can’t choose keywords anymore,” Srinivasan said. [...] When you search for a product, Gemini writes a custom explainer for the advertiser, framed as objective advice about why this product “may be the right choice for you.”
I have a theory that the people who are naturally good at AI are the ones who spent years translating their own thinking for systems that weren’t built for them.
That’s a lot of women. And that’s a lot of neurodivergent people. Especially the high-masking ones who got so good at it that nobody noticed they were doing it at all.
The new Anti Gravity from Google competes heads-on with Claude Cowork from Anthropic - it's aimed to regular users not engineers. It's basically a chat that can run code on your computer, which it turns out can be exceedingly useful. (You can still get their actual code editor separately.)
Andrej Karpathy joins Anthropic.
Models are both becoming much more useful, and pricier. Anthropic was the expensive but very good model, then OpenAI increased prices for their best models, now Google is increasing pricing. Not a little, think 3X increases. This represents demand and somewhat limited supply: the latest models are so good at coding (agent tasks) that demand is through the roof, and paying $200/month, or much more expensive API pricing, doesn't seem too crazy. This also gives the labs a strong lever to keep you in their ecosystem - use their own tools, and you can use a subsidized subscription model.
Anyone that has done qualitative user research (a lot of UX researchers) thinks the whole “synthetic users” thing is mostly useless and possibly actively dangerous. Bain published a more positive report that’s not terrible and worth a read. And in the spirit of keeping an open mind (and models become better fast), it may be that these things are becoming useful. I am still doubtful/conflicted about the real-ness of any insights you might get from AI representing users though. But if emotions are represented realistically in LLMs, maybe synthetic users can become a useful thing.
A key role for UX departments right now: track and understand how humans use AI, and how expectations are evolving quickly. Anthropic publishes some useful research regularly, openAI now also published some (high level) data.
Our key finding is that these representations causally influence the LLM’s outputs, including Claude’s preferences and its rate of exhibiting misaligned behaviors such as reward hacking, blackmail, and sycophancy. We refer to this phenomenon as the LLM exhibiting functional emotions: patterns of expression and behavior modeled after humans under the influence of an emotion.
Interesting research on emotions in Sonnet 4.5
Richard Sutton: (Linking to the new Digg since I want to avoid X)
The bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
A lot of AI adoption is still happening under soft economics.
Flat-rate subscriptions. Promotional pricing. Cloud credits. Internal experimentation budgets. Strategic subsidies. Bundled features. Investor-funded usage. Enterprise trials. Internal mandates to test AI.
Just one small quote from this excellent long essay
It can take a second
There's a lot of infrastructure (database calls, page loads, etc.) that has been optimized for humans and needs to return in, say, 200ms, or 20ms.
I'm often building for agents now, and the latency requirements are different, which often also means you can figure out an architecture that makes things 100x cheaper to run. If the agent is running in the background it’s often ok for something to take a second.
For example (and this is less latency focused but hey), https://clawfeeds.com/ writes markdown files to an S3 bucket, which then is essentially free to serve to agents.
Skills (a markdown file with optionally some scripts), running on an LLM in a loop on a box, feel like the minimal viable and cleanest possible implementation of an agent.
It turns out a "bliki" (a blog combined with a wiki) is actually a thing (see Martin Fowler) and I am realy enjoying it.
Digital Garden https://maggieappleton.com/garden-history the wiki capability I added a while ago turns this from a blog in a Digital Garden and that feels nicer
How to organize meetups https://maggieappleton.com/gathering-structures
An interesting Multi Player experiment (I lost the link): a Slack-like environment where agents and humans collaborate. (This was out of the Github team)
Woops I just realized mentioning people with their name in wikistyle makes sense. Simon Willison, John Cutler
John Cutler on team patterns with AI.
Single Player - one dev with agents and context.
Shared Context Solo - each dev solo, but shared context files
Multi Player - collaborating on the work, in addition to shared context
UX: orchestrating and managing many agents
Business: charge for outcomes
Tech: server less cheap and fast “containers” that can run a bit of code
Cloudflare’s agent stuff and our own work made me think, a lot of the work with agents is in figuring out and inventing the right New Primitives for this new world. Technical but also UX and business (“charge for outcomes”). Which I find interesting.
The past 2 weeks I've been heads-down in building out the document pipeline for Sputnik Legal.
Inspired by IKEA, the goal here was: do the pre-processing and analysis for under $1/GB. Pricing first is key to our competitive advantage.
Processing documents is a whole thing, luckily I have access through our past work to many, many gigabytes of actual legal documents - many are really crazy. Scanned TIFFs of documents. Encrypted PDFs. EML files. 600-page documents. You name it, we can usefully process it now :)
Claude Code + observability is wildly powerful. You can set up automated production bug tracing (what is the issue, what caused it, who did it affect, all the detail). And then feed that back into Claude, who (ok) with that level of detail is very, very good at just fixing the issue.
I wonder what the equivalent for product and design observability will be. Product metrics, yes, but there has to be something more.
If you are a leader in UX or design right now, in an organization, one thing you can do is Increase Communication.
That means: create more opportunities for your team to have spaces to talk about AI.
Especially cross-functional communication, discussions with technology, discussions with product, etc. A time of change (with AI) is not a time for silos.
The pattern in Claw Feeds is that there are no user accounts (that might change if we figure out agent accounts one day), a human never has to visit the site, the agent can just do stuff.
Usability Testing is still a very useful skill in the AI times, and best learned by joining someone who is good at it.
My kid was explaining me the brain (university study), and it’s such a multi LLM system
Claude And Paper also helps with Decision Fatigue when doing Agentic Engineering (can you tell I’m enjoying my wiki?)
I build Claw Feeds (https://clawfeeds.com/) for myself, and it's really really useful if you are building content operations with AI.
It's essentially an RSS feed reader for agents.
It reads the feeds, and creates very token-efficient markdown files from them that are easy to parse.
It's free and agents can use it directly.
https://modal.com/ Modal is really interesting. It's effectively a very developer-friendly and affordable way to run Python on a CPU or GPU for a few minutes. You pay by the second. I set it up with Claude knowing no Python whatsoever.
How to set culture
Quick thought. This is what I have seen work. When you set culture from the top, things that work are:
You as the leader talk about it to new hires. You can batch them. And mention examples. New hire is when people learn what kind of company they joined.
When a tricky decision needs to be made, most people will default to How This Is Handled In Companies. That is, to how they think this is normally handled in companies. This is your opportunity to say, no, WE handle it like this, because we believe these things. That's what really sets culture.
You as the leader talk about it in the all hands, in the context of decisions the team is making.
Feed of personal blogs. And another feed. Are Blogs Back
Looks like blogs are somewhat back - some blog discovery places. Are Blogs Back
Engineers throughout are being really surprised about how they can now take on projects they would not have considered before. It seems like Be More Ambitious is the pattern.