194 posts tagged with “process”

Karo Zieminski, in her newsletter Product with Attitude, is writing for builders, founders, and PMs, but the design translation is straightforward: AI fluency without critique is just a faster way to lose your ability to evaluate the output for yourself.

She draws the distinction:

Plain AI literacy means knowing how to use AI tools. It means learning to prompt, create automations, and bring AI into your workflows.

Critical AI literacy goes further. It adds systems awareness: understanding that AI is not just a tool on your screen, but part of a larger system of model choices, product decisions, business incentives, policy constraints, ethical tradeoffs, and human consequences.

Attitude is the posture that turns AI literacy into critical AI literacy.

That word, posture, matters. It is the same split in not outsourcing the learning: the tool doesn’t determine whether you get sharper or softer; the way you use it does.

Zieminski puts research behind that concern:

The data is on the table now. Microsoft Research surveyed 319 knowledge workers in 2025 and found that higher confidence in the AI is associated with less critical thinking, while higher confidence in yourself is associated with more. MIT Media Lab’s “Your Brain on ChatGPT” study measured the same erosion at the level of brain activity. The muscle is real, and it atrophies on schedule.

For designers, the warning is: don’t let the tool do so much of the looking, choosing, and checking that you stop building those muscles yourself.

Zieminski defines the working posture plainly:

AI with attitude means using AI with judgment, boundaries, curiosity, and scrutiny. You enjoy powerful tools without worshipping them, panicking about them, or letting them decide how you think, work, create, and learn.

Sen Lin, writing in UX Collective, has a useful reference for designers who want Claude Code to fit their workflow instead of acting like a generic engineering agent:

Claude Code is an agent, and an agent is only as good as how you configure it. That’s the part worth paying attention to as a designer: Out of the box it leans engineering, but you can set it up to fit how you actually work — to understand design, respect your workflow, and carry the right capabilities — so it produces better results, faster.

A technical stack is the set of technologies you decide on before writing a line of application code. A design stack is the same idea, applied to Claude Code: the brief, the design knowledge, and the tools you hand the agent upfront.

The useful move is treating Claude Code setup as a design operation, not a prompt-writing trick. Give it a project brief, a design-system guide, and the right tools before it starts touching the work.

If CLAUDE.md is the brief, DESIGN.md is your design system translated into something an agent can read and obey. It’s a format specification that describes your visual identity to a coding agent: the exact values, and the reasoning behind them.

The file works on two layers, and both matter.

The first is machine-readable design tokens, written as YAML front matter — the exact values, so the agent never has to guess what gray-500 resolves to or which surface color a modal should use: […] The second is human-readable design rationale, written as prose — the part that explains why a value exists and how to apply it. Tokens tell the agent what primary is. The prose tells it there should never be more than one primary button per view, that a primary action is always paired with a neutral or subtle one for cancel, that button width is set by the parent and never hardcoded.

The tool/instruction split is the part worth saving:

MCP gives the agent access to tools.

Skills give it the knowledge to use those tools well.

Where MCP is access, a Skill is a markdown-based guide that teaches Claude how to perform a specific task in a specific way. It’s a set of instructions, scripts, and resources — a training manual that keeps the agent from wandering down expensive detours and burning tokens on the wrong approach.

Good checklist. The designer’s job is not just to ask Claude Code for UI. It is to onboard the agent into the product, the design system, and the working style before generation begins.

Designer and writer Christopher Butler is writing about AI, but this also lands on the mechanics of agent loops. A loop can keep running, but useful output still depends on the constraints, preferences, success criteria, and taste made explicit before it starts.

Butler, on what stays human:

The more interesting situation is the one where we keep the thinking and hand off only the doing — and what happens when we do.

What happens is more than speed. When you have to describe what you want to the agent — with enough precision that what comes back is what you actually wanted — you begin to think about the thing differently. Description, it turns out, is where the idea often actually gets made. You start by knowing roughly what you want, and the act of articulating it produces a clearer want, which produces a sharper specification, which produces a better thing. Then you do it again. The thing improves; so does the thought.

That maps to the AI bottleneck I keep seeing: production speeds up, but judgment still happens at human speed. Butler isn’t arguing for slower tools. He’s arguing that useful AI makes the thinking more explicit, not less.

The friction is part of the point:

The agent’s current maturity requires a level of input precision that a competent colleague does not. At first, this can be a frustrating blocker; we’ve depended upon a different kind of intelligence in our peers — the kind that requires no elicitation. The machine does. But in a sense, this is a gift. It forces you to think the thing through in places you might otherwise have left fuzzy.

That’s the designerly translation. The machine doesn’t infer your hierarchy, tone, edge cases, or tolerance for risk unless you put them somewhere it can use. That friction is useful because the agent forces you to specify the fuzzy parts before it starts generating the same mistake everywhere.

Butler’s word for that retained work is investment:

This system depends upon me to provide the thinking. The agent does the doing. The structure is, I think, the practical form of the argument: the systems carry the doing, and they carry it well precisely because I have spent the time to think them carefully through. We like to call this intellectual property, which I think is a bit obnoxious. It’s really intellectual investment. Every technological advance should be measured not by the measure of intellectual property it absorbs — how much it can do without us — but by how much intellectual investment is worth sowing in it.

That’s the useful correction to “AI will do the work for us.” It will, but only the part of the work that can be expressed well enough to delegate. For a design system, that means brand rules, component logic, editorial standards, and good taste have to become rules, prompts, specs, tokens, and checklists the agent can actually use.

That leaves the tool in its proper place. Using AI lazily will produce plenty of forgettable output. The more interesting use treats it as a medium that rewards the thinking you put into it.

Of course, there is tension between expressing our specs as words and spatial manipulation as visual thinkers.

Claire Vo on her How I AI podcast gives a plain-language walkthrough of agent loops in Claude Code and Codex. The episode is technical, but the useful translation for designers is familiar: define the job, give it tools, decide what good output looks like, and make the system prove it. Once an agent can run without you, the interface problem moves into instructions, permissions, and checkpoints.

And then a couple other foundational things that I think are helpful when you’re running loops. And why are these things helpful before we get into what they are? They just keep the work clean.

If you are going to be yolo-ing loops all over the place, you’re going to want some consistency in execution, you’re going to want clean workspaces, you’re going to want conflicts resolved and avoided. And so, all these things are really to make those loops effective.

And so, what are the things? They are work trees. I feel like this entire podcast could be Git 101. But work trees are just basically a way to isolate the work, especially the coding work of an agent away from other agents’ work in a sandbox. There are skills, repeated ways to do common tasks. We have a full episode on what skills are from earlier last year when they came out. Plugins and connectors, these are just the tools that your agent has access to. […] Sub agents, both Codex and Claude Code allow you to kick off sub agents. This is just a way to federate out work from the main thread, so that sub agents can do specific tasks, especially validation. And then there’s some way to track state. And essentially just think of this is like a to-do list.

The practical translation for agent orchestration: write the operating conditions around the work before asking for the artifact.

Calling it a loop makes it sound like automation plumbing. The design work is the brief: what the agent can touch, what tools it can use, and how it knows it is finished.

The employee comparison makes the loop feel less exotic:

Now, people are going to ask, “What should I use a loop for?” And when you’re designing loops or designing agents, I say this is the time for the manager. You are designing a job. And so, just imagine that you’re onboarding an employee. That employee could be an executive assistant. That employee could be a customer service agent. That employee could be a software engineer.

Every Friday, EA, I would like you to review my calendar, see who canceled on me, where I could have used my time more effectively, if there are any follow-ups, and send me a Slack to get this done. Um and I want you to do that every Friday. Guess what? You’ve just designed a loop for your executive assistant. […]

So, I really like to think about loops as designing workflows and designing jobs to be done for people. It just happens to be that you can put this intelligent agent against the loop, and then it’s ready to go.

For designers, that last sentence is the translation layer. A designer already knows this move from creative direction: clear constraints produce better work than a blank request. The only difference is that the worker now happens to be software.

A loop without success criteria is just a faster way to make someone review the same ambiguity at a higher token cost.

We all want our agents to work for us on a schedule whenever we want, doing work that we don’t want to do. It’s great. What are some of the problems? One, loops can get expensive. So, I just kicked off an automation that happens on a regular basis. It does wide-ranging work. It decides when to spin off sub agents.

And it does loop-based validation, which means it’s burning tokens until it hits a threshold that it decides is successful. If you do not write that loop well, or your validation criteria is too thin, guess what? Your agent is going to burn tokens. I think we’ve seen this with open claw in particular or some of these agent harnesses is they’re really good at loops. They’re very diligent. They get interesting work done, but man, do they love to burn tokens.

[…] Loop-based prompting is just its own thing. Goal-based prompting in specific is just its own thing because you have to be very precise about evaluation and success criteria. If you are not, you will be very disappointed and use a lot of tokens for not a lot of output.

Jenny Xie, writing for Figma, talks with Vice President of the Pantone Color Institute Laurie Pressman about color as a cultural language, not a decorative layer. Pressman’s starting point is that color is read, not just seen:

Color is tied to our emotions and how we interpret the world. It’s a language that reflects what’s taking place in culture.

The practical questions Pressman gives designers are “What message am I trying to convey?” and “how do I use color to help me get there?” That’s the design-system version of brand strategy: what does this choice need to make legible before anyone reads the copy?

Xie follows that with examples where the same hue changes meaning across cultures, media, and materials:

Like any language, the language of color comes with its regional dialects. While black is worn at funerals in the West, mourners in the East wear white. Depending on the context, red can convey love, anger, or urgency in the West; in the East, it’s associated with luck, prosperity, and celebration. Brand and marketing teams can’t just follow color trends or assume universal meanings. “Someone seeing a product on the shelf in Japan versus France is going to have a different color sensibility,” says Laurie, “because they’ve built up certain associations.”

Pressman also brings this back to process:

Colors don’t exist in a vacuum. They show up differently depending on the material, medium, or surface. Something that resonates on a screen may look garish in person; a hue that exists in fabric dyes may not be achievable on, say, a Band-Aid. “This is why you must consider color at the inception of the design process,” Laurie advises, “so you can make decisions with full context of the material and surface finish on what it will appear.”

There are two versions of the same design-systems worry. One is about craft: AI can make an interface look considered without teaching anyone how the system works. Design-systems expert TJ Pitre pushes on the governance version: once the system is machine-legible and agent-friendly, who owns the calls the machines are allowed to make?

So when I say I have a beef with “agentic design systems,” understand that it isn’t a beef with agents. It’s a beef with one specific move that the term smuggles in, and that most people repeating it haven’t noticed they’re endorsing.

Here’s the move: handing the judgment layer of a design system to an autonomous agent loop that no human owns.

That’s the whole problem. Everything else is just tooling, and the tooling is great.

Design systems as AI infrastructure only works when the infrastructure still has an owner. Make the system machine-readable. Let agents generate, document, test, and check against it. But Pitre is correct that the library is only useful if the standards still have an accountable owner.

Strip away the Figma libraries and the Storybook instances and ask what a design system actually is. It’s a set of decisions an organization has agreed to and committed to enforcing over time. What does “primary action” mean here. When do we break our own grid. Does this thing deserve to exist as a component at all, or are we about to enshrine a one-off into the canon forever.

Those aren’t generation problems. They’re judgment calls, and they carry consequences the organization is accountable for, to its users, its engineers, its brand. A design system is, underneath all the tooling, a way of encoding collective judgment and holding people to it.

Pitre turns that into a test:

Which gives you a clean test for any “agentic design system” claim you encounter. Ask: what rejects the agent’s output, and who decided the rule it’s being rejected against? If the answer is a human-owned gate, it’s the real thing. If the answer is “another agent checks it” all the way down, you’ve built vibe coding with extra infrastructure and a more confident logo.

And that version is arguably worse than a person vibe coding in a scratch repo, because it launders drift through the authority of the system. The output looks sanctioned. It came from “the design system.” Nobody chose it.

In the end the invisible hand of the designer must still be felt.

Murphy Trueman isn’t worried that AI makes bad design. She’s worried that it can make passable designs, enabling designers to bypass contact with the material that teaches them how systems actually behave:

UI is starting to feel disposable in a way that unsettles me. Not as a complaint about quality. Structurally.

The pace of generation means nobody dwells in a screen long enough to notice whether it’s actually considered or just plausible, and plausible is increasingly good enough because the bar for “does this look designed” keeps dropping when the tools do the designing. The decisions are still there, technically, but the attention that turns decisions into craft has somewhere else to be.

You can produce a lot of considered-looking work without any of it being considered.

This is the craft problem hiding inside the productivity story. The danger isn’t only that teams will ship screens faster. It’s that they may lose the habit of noticing the small decisions that make an interface hold together after the first glance.

Trueman gets more specific about what Figma gave her:

But the specific thing that made Figma matter to me wasn’t efficiency. It gave me a way to hold abstract structural ideas in my hands — the relationship between a component and a style, between a token and a decision, between a change made in one place and every instance that inherits from it, made visible, made editable, made something you could touch and understand by touching it. That’s how I learned to think about the work.

That’s the part I don’t want AI tools to flatten. The best design tools don’t just make results; they make relationships inspectable. You learn by manipulating the system, getting it wrong, and seeing which decision moved. Difficult to do in code and see it cascade in the product.

A Singaporean software engineer who goes by Airing is writing for devs who can already see the AI curve from inside their own work: docs, code, review comments, all moving faster than the human capacity to verify them. The design translation is pretty direct. AI changes who can produce, but judgment stays human, and the more production accelerates, the more expensive weak judgment gets.

Over the past year, every piece of my work that I could hand off, I handed off to AI, piece by piece. The design doc — it wrote. The code — it wrote. The first drafts of documents and review comments — it wrote. What I can now run in parallel in one evening would have taken a full quarter two years ago. By rights I should be idle, but the truth is the opposite — I am busier than I have ever been. The content of “busy” has simply changed: I almost never produce anything with my own hands now. I spend the whole day reviewing what it produces.

This is the embryo of the end-state workflow. In it, there are only two things left for humans to do: review the design, and verify the result.

Notice what these two have in common: neither of them is production. They are gatekeeping.

That changes the clock as much as the craft: more time spent checking outputs, less time making the first pass by hand.

That gatekeeping word applies to designers too. When AI can generate the interface, the screen, the copy, and the prototype, the remaining work is deciding whether any of it should exist in that form. Airing says the temptation is simple: “lower your own standards.”

Read one fewer line of code. Skip one step of reasoning. Don’t ask one of the “whys.” When the little voice in your head says “eh, probably fine,” nod and let it pass. Project this forward and you can already predict the next few years: nearly every collapse in engineering quality will not be because one specific person was lazy — it will be structural. When production is infinitely fast, the verification standard becomes the only compressible variable in the system, and every incentive in sight pushes you to compress it: the boss’s expectations, your peers’ speed, the performance-review whip — they’re all telling you to let go.

But think clearly about what letting go means. The reason you have not yet been optimized away from the verification step is precisely that your standard is higher than the machine’s. A gatekeeper who keeps lowering the standard is personally building the case for their own replaceability. My stance on this has never moved: slower is fine. The standard, not one inch.

Airing refuses to make this only a productivity story. The professional risk is cognitive atrophy: forwarding the problem to the model, forwarding the answer back, and losing the stretch where judgment is formed. For designers, the warning runs through the same place: don’t outsource the taste-building, the principled refusal, or the logic of why a thing should be this way and not that way.

A real question, most of the time, does not have a deterministic answer. Cognition does not fall from the sky. It is ground out by humans through derivation, argument, and exchange — bit by bit, through a process that cannot be skipped, because that process is the human. If cognition is handed to you directly by AI — whether or not it is comprehensive, whether or not it is critical — the mere absence of that middle stretch of thinking is enough to let subjecthood retreat, inch by inch, until what is left is the silhouette of someone waiting in front of a Jenny to be retired.

AI cannot replace humans in perceiving and experiencing the world — and this isn’t a pep talk; it has a structural basis. I wrote, in an earlier essay on AI and psychological healing: human cognition is not just information processing — it is an experience interwoven from sensation and emotion. We perceive the real world through our bodies; we assign meaning through feeling. AI is unmatched at processing information and generating text, but what it lacks is precisely this dimension of experience — it can process data, but cannot experience the real world that the data represents. In one line: AI’s understanding is instrumental, not existential. It can help us understand certain facets of the human condition, but it cannot replace the insight we obtain through lived experience and inner reflection. A painting can imitate nature, but it can never become nature itself.

The reed’s entire dignity is in its thinking. Please do not hand it over.

Cash App product designer Brad Wrage on moving design from handoff to production ownership:

Across this project, I personally merged 25 pull requests across three codebases — Android (14 PRs), iOS (8 PRs), and Server (3 PRs). As a designer.

And zooming out further — over the last two months, I personally authored ~45 PRs merged across 5 repos. As a product designer:

Wrage stayed accountable for the experience after Figma, across Android, iOS, and server. That is where the delay in a traditional handoff becomes obvious. It maps to Cash App’s org-speed bottleneck: building faster only matters if approvals, reviews, and deployment move with it.

Wrage describes the operational core:

This is where the traditional dynamic flips. Instead of filing bugs and waiting for engineering to prioritize them, I lead the charge — logging issues, kicking off fixes, and drive them to completion.

The process: visual feedback and bugs drop into a dedicated Slack channel. Builderbot — which intimately knows our codebase — picks them up and posts rapid fixes, often within minutes.

[…]

I pull the code down, test on a real device, reference my Figma file via MCP, and approve or adjust until it’s right. Every PR still gets a human review.

The guardrail is in that last sentence: code review remains part of the process. The result is a tighter loop between taste, implementation, and product judgment without removing engineering accountability.

Wrage’s phrase for that distance:

This is the inflection point. The designer isn’t on the sidelines filing tickets anymore — the designer is in the driver’s seat, leading the last mile to ship.

The last mile. The difference between “shipped” and “crafted.”

B. Prendergast revisits atomic design:

Atomic design is nearly ten years old. Brad Frost’s atoms, molecules, organisms hierarchy did something genuinely useful when it arrived: it gave teams a shared language for the idea that components are made of other components, all the way down. For a lot of people, including me, that was the conceptual unlock they needed to start building modular reusable front ends instead of pasting the same button in seventeen places. It worked. The industry absorbed it wholesale.

But I think we can probably retire the metaphor now.

Brad Frost’s watershed atomic design concept solved a real communication problem. It helped designers and engineers talk about components as things that compose, not screens that get pasted together. The problem starts when a teaching metaphor becomes the operating model.

Prendergast describes the tax teams pay when that happens:

The problem I keep running into with teams over the years isn’t that they don’t understand atomic design. It’s that they’ve understood it too literally. They’ve set up their Figma and Storybook libraries with Atoms, Molecules, and Organisms sections. They’ve had the naming convention conversations. And then, reliably, they get stuck arguing about whether a card with an avatar and a label is a molecule or an organism. That conversation is a tax. It doesn’t produce better components. It produces friction, disagreements, and the occasional afternoon lost to taxonomy instead of building.

That’s the part I care about in design systems. A good library should make the useful path obvious: which pieces can be reused and which contracts they have to honor. If the library mostly teaches people to sort objects into clever buckets…well, that’s a distraction from the work.

Prendergast closes with the on-ramp caveat:

I’m not arguing against atomic design as an on-ramp. If someone on your team is new to component-based thinking, the atoms/molecules/organisms scaffold is still a powerful and reasonable way to introduce it. Use it. Just don’t park there. The abstraction to keep is composability. Components compose up from smaller components. There are no real required levels between the smallest useful unit and a whole page or view. Whether an intermediate component is a molecule or an organism is a naming problem, not a design problem. We can stop solving it now.

The real test of a design system isn’t whether every component sits neatly in the correct layer of a hierarchy. It’s whether people can understand it, trust it, use it, and extend it without breaking it.

Nobody ships a better product because they correctly identified a component as an organism instead of a molecule. Atomic design succeeded because it taught people to think in terms of composition. Components built from components. Small pieces combined into larger ones. Clear contracts. Predictable behaviour.

Everything else is bookkeeping.

Jason Cyr, writing at The Human in the Loop, starts with the AI-design conversation’s taste claim and points to the work teams need before agents start producing anything:

There’s a popular narrative that design’s value in the age of AI is taste — the human eye that says “not that, this.” I think that undersells us. Taste matters. But what organizations actually need from design right now is clarity. The ability to wade through ambiguity, make invisible systems legible, and give teams something they can act on. That’s always been the real (often under-appreciated) superpower, and AI just made it an urgent need.

Cyr’s earlier piece on agentic-era design teams covered the move from making outputs to directing work. Here, he describes the coordination layer that had been hiding inside the old process:

The old product development process had shock absorbers we never realized. Meetings where people quietly aligned on things that were never written down. Hallway conversations that resolved ambiguity nobody had formally surfaced. Design reviews that were really translation sessions — designers decoding what product actually meant, engineers decoding what designers actually intended. PMs who held critical context in their heads and dispensed it as needed.

None of this was in any process document. It was human labour — invisible, unacknowledged — absorbing the ambiguity that the formal process couldn’t handle.

We called it process, but it was actually a buffer.

Cyr puts that clarity work inside design leadership:

Here’s the thing about this problem: it’s not a tooling gap. It’s not a project management gap. It’s a clarity gap.

Design earns its seat at the table when it moves beyond artifacts and starts shaping how a product organization delivers work. Not just the screens. Not just the system. The operating model itself — who decides what, when something is ready, how context travels, and what “good enough” means at each stage.

Amber Bouabdallah, writing in UX Collective, gets at the learning problem lots of designers are facing in this AI transition: the tools don’t produce one shared path toward competence.

Bouabdallah draws the line between deterministic software training and relational AI practice here:

Traditional software training works because the tools are deterministic. You learn where the buttons are, what the shortcuts do, how the system behaves when you click the thing. Mastery, in that world, converges — everyone arrives at roughly the same competence, following roughly the same path, and you can write a training deck for it. Mastery means knowing the tool’s correct use.

AI tools break that definition. Maggie Appleton — designer and anthropologist, now at GitHub Next — gave a talk in 2023 called “Squish Meets Structure” about designing products with language models, and the line from it that I love is her description of the magic-input box: it has “no affordances,” “no knobs or door handles.” The interface, she writes, “offloads a ton of cognitive labour to the user.” There is no correct use to learn. The tool meets you where you are. Which means what you bring to it — your instincts, your mental models, your accumulated taste, your willingness to iterate, your custom claude.md files — is the tool, as much as the model is.

So mastery hasn’t disappeared. It has shifted. With deterministic software, mastering the tool meant converging on its logic. With AI tools, mastering one means the opposite: learning to bend it toward your logic. Tailoring it to how you already want to work. Mastering an AI tool is the craft of making it amplify the specific strengths and experience you bring — so the work that comes out is sharper, and unmistakably yours. That kind of mastery is real, and hard-won, and worth teaching toward. It is just personal rather than universal. Divergent rather than convergent. Everyone’s version of it should look different, because everyone’s version is built out of a different person.

Her Salesforce examples keep that from becoming an abstract tool-training claim:

Six months in, Ningdan and I had designed for tool adoption and accidentally created conditions for something more intimate. Seeing each other work. Seeing the specific choices someone makes when the tool doesn’t behave, the workarounds they’ve invented, the mental models they’ve constructed to make sense of something genuinely new. A window into someone’s thinking — and into how each person was mastering these tools in a shape no one else’s would match.

And once you can see the thinking, you can see the worry too. Amanda Harris, a User Experience Architect on our team, named a tension directly in the post-mortem: “I worry that we’ll lose the exploratory aspects of finding what’s wrong with an idea by jumping so quickly into hi-fi prototyping.” That’s not resistance to new tools. That’s a designer protecting something she knows matters. Hearing it voiced — in a room where everyone is nominally learning the same things — is only possible in a setting small enough and safe enough for honest uncertainty.

The anxiety designers are feeling is a signal, not a weakness.

And Bouabdallah closes by naming the training layer her team actually designed:

The tools will keep changing. They will keep arriving faster than any module can be written, any best practice can be documented, any official curriculum can ratify. It is tempting to treat the peer layer as a bridge — something to lean on until the real training arrives. But the real training is not coming, because there is no fixed competence to train people toward. As long as the tools keep moving, the peer layer isn’t the bridge. It’s the ground.

We did design how designers master AI. We just found that mastery wasn’t what we thought it would be. Not a competence everyone arrives at, but a practice each person builds — bending a generic tool toward their own strengths, their own experience, their own way of working, until the work it helps them make is sharper and theirs.

Jakob Nielsen starts from OpenAI’s new $4 billion consulting arm and its acquihire of 150 Forward Deployed Engineers, the kind who embed with a client instead of building from headquarters. His argument is that they solve the wrong level of the problem: you can speed up the work without changing it. The missing counterpart he proposes is the Forward Deployed Designer.

Nielsen draws the line between faster individual work and a faster business:

With AI, the old workflows must no longer be treated as the design brief; they must be questioned at the root. AI is, in fact, a great productivity enhancer even when used in the traditional way to increase the efficiency of individual employees performing the same tasks as always, just faster and better. A paralegal can summarize a legal brief in seconds; a junior developer can write boilerplate code instantly; a digital marketer can generate campaign copy with a single prompt. We can typically improve that employee’s performance on those specific rote tasks by roughly 40%.

But at the company-wide level, such local productivity gains rarely translate into substantial profit gains and shareholder value. When you have a long chain of steps and optimize only a few, the delay simply shifts to the remaining steps, which will dominate the overall time to solve the underlying problem.

Nielsen again:

Once AI removes the cognitive bottleneck, a different bottleneck appears: authority. The limiting question becomes not “Can the system know what to do?” but “Is the system allowed to do it?” AI-native workflow design must therefore redesign decision rights, escalation rules, audit trails, and accountability boundaries. Otherwise, the organization merely replaces slow human cognition with fast machine recommendations waiting for the same old human permission structure.

The line usually attributed to Einstein goes like this: “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.” It is a warning against racing to the answer. Gale Robins, writing for UX Collective, makes the same case for product teams—then moves it one step earlier than Einstein did.

Her subject is the decision that rarely gets scheduled: whether a customer signal deserves discovery at all. She calls it Signal Evaluation, and she describes it bluntly:

Signal Evaluation is not a box to tick on the way to the real work. It is a filter, and a good filter is defined by what it keeps out. If most of what reaches your team makes it into discovery, the filter is not working; it is just a turnstile.

This is Einstein’s 55 minutes, relocated:

It is tempting to think discovery begins when you start talking to customers. It begins one step earlier, with the call to research this rather than something else. Every signal that reaches a team […] arrives carrying an implicit claim: this is worth your attention. Evaluating a signal is the act of testing that claim before you spend anything on it.

The difference she leans on is between a feature signal and a job signal:

The distinction that matters is whether the signal is about the customer’s job or about your product […]. “The export button is confusing” is a feature signal: it concerns your solution and usually warrants a quick fix rather than a discovery effort. “I cannot get my insights to my stakeholders” is a job signal: it is about what the customer is trying to accomplish, and it may hide an underserved need worth real investigation.

That is the trap Einstein was guarding against. The seductive request arrives pre-packaged as its own solution—add the widget, fix the button—and it is tempting precisely because it lets you skip the 55 minutes. Robins’s point is that a signal can be strong, genuine, and still aimed at the wrong job.

Here is where she goes past Einstein. His hour is already committed: he has a problem and is deciding how to spend time inside it. Robins is working a layer earlier. Her question is whether the signal has earned the hour in the first place. In her words, the skipped judgment is “whether to begin it at all.” Signal Evaluation is the gate before Einstein’s clock even starts.

AI is what makes that gate matter more now, not less.

AI can scan your entire feedback corpus, cluster signals by frequency, surface correlations with churn, and tell you in minutes that the widget request appeared in twenty-three of forty calls. That is genuinely useful and genuinely predictive: pattern-finding at a scale no human can match.

But notice what AI cannot do in that example. It can tell you the signal is strong. It cannot tell you the signal is pointing at the wrong job. That judgment, that “more widgets” is really “I cannot see what matters,” and that building the literal request might make things worse, is meaning-making, not pattern-matching.

Tokyo Design Forum publishes former Facebook and Dropbox design leader Soleio’s closing talk from this year’s conference, where he previews a book-sized thesis called The Geometry of Luck. Soleio’s move is to treat luck less like a mood and more like a design problem: a question of arrangement, position, and conditions.

He starts with geometry because geometry gives the argument its discipline:

When we say something has a geometry, we mean it has structure.

Not just parts, but relationships between parts, distances, angles, arrangements that produce specific properties.

For example, a triangle just isn’t three lines, it’s three lines whose arrangement do something. The interior always sum to 180 degrees, no matter the triangle. That is geometry. It’s structure that produces reliable properties.

So when we talk about the geometry of a room, or the geometry of a negotiation, or the geometry of a network, we’re saying something very specific about its nature. We’re saying its composition, its arrangement, tells us more than a list of its parts.

That saves the talk from becoming another self-help riff on “making your own luck.” Soleio is more precise than that. He is saying luck has variables, and designers already understand the work of arranging variables toward a purpose. He links that to industrial designer and architect Charles Eames’s definition of design as a plan for arranging elements toward a purpose.

When something has a geometry, it can be measured, it can be reasoned about, it can be taught.

[…]

So geometry is a language of arrangement.

For designers, it’s like the vocabulary of our craft.

From there, the framework becomes practical:

I believe luck has three facets, three independent variables that work together in concert. They are the basic elements of good fortune.

The first I call orientation, how we perceive our environments and place ourselves within them.

The second is surface area, the degree to which we’ve made it easy for good fortune to find us.

And the third is novel action, our capacity to act on what we perceive, what we do with the opportunities that the universe presents to us.

The useful distinction here is agency without control. You don’t command the outcome. You arrange the conditions: what you can see, who can find you, and whether the value you create can keep circulating after it leaves your hands.

That is why the talk eventually turns back toward software design:

As software designers, we shape the environment where luck happens.

We are very, very lucky to be here in this room today.

Few inventions touch the fabric of people’s daily lives, such as software.

Every interface, every space, every system we create either amplifies or dampens the flow of opportunity for the people who encounter it.

With every over-the-air update we push to production, we alternate the networks through which luck flows.

And so I hope designers put as much consideration to luck as they do look and feel and utility.

I like that as a design brief. Not “be lucky.” Not “hustle harder.” Arrange yourself, your work, and your systems so more good fortune can pass through them. Test hypotheses. That is a useful bar for products too.

The line running through Tobias Van Schneider’s interview is simple: designers complain about tools all the time, but the better move is to build the environment you wish you had.

Nikolas Wrobel interviews designer Tobias Van Schneider, founder of Semplice and mymind, and the profile traces that pattern directly: Semplice for portfolios that didn’t fit the platform/template world, then mymind for thinking without the performative noise of social media.

Van Schneider:

How do you protect yourself against consuming, draining effects from Social Media, or disenchanting tech-mechanisms?

This question almost too perfectly leads into what I do everyday. In part, I protect myself by using mymind.com (which I created) — there are no ads, no vanity metrics, no social media features, nothing but myself. Only me and the things I care about. Over the years, mymind has become so valuable to me, it’s the first place I go to look for inspiration. In fact, while I am answering this interview, I find myself going back and forth between old notes and musings inside mymind.

Social media still drains me, just like everyone else, but it’s nice to at least have one place just for myself. And thats mymind.

Aside from that, I just get offline and out into the world. Or I create something.

Van Schneider isn’t pitching another social layer; he’s describing a private room for memory, taste, and reference.

He later explains where that product idea started:

As with many things, it was a total coincidence. When we initially worked on the mymind product and brand, we didn’t even have the name “mymind” yet. The whole thing was called AWMT, which stands for “As We May Think” and is a reference to an old essay by Vannevar Bush from 1945 in which he wrote about a machine called “The Memex” which was some sort of machine that collects and connects your personal knowledge.

Coming off of that inspiring essay, we came up with a slogan called “Think for yourself” which is sort of the antithesis to the cloud/hive mind of what we call social media today. Especially since we position mymind as a private sanctuary, it just made sense to us.

All of this eventually got me into the rabbit whole to search for ideas for our logo. The classic “Thinker” statue immediately came to mind. I always loved that one, a man deep inside his own thoughts, unfazed by the world around him. But the statue was a bit too literal to me, too well known, too sharp and serious. We needed something more abstract, more playful. Eventually I found out about Cycladic art, originating from the Aegean islands during the Early Bronze Age. Very famous for their minimal and stylized marble figurines. Now, the rest is history. I immediately fell in love with the simplicity of it and it felt like a great canvas to build our visual universe on it. The rest is history (:

Wrobel asks Van Schneider about the conditions he works best under:

The creative me enjoys being alone. Completely isolated, nobody even in the other room. It gives me freedom and clarity to think for myself and be myself. My real creative being thrives in these moments, untouched by the opinions and desires of others.

My ultimate solitude tends to arrive at midnight. It almost transforms me. The dark brings focus. Silence brings new ideas. No voices interrupt, no chance of emails, just me and my thoughts. It’s this time I feel most creatively alive.

Now, add a soundtrack to it and I’m in creative heaven (:

I don’t think every designer needs midnight solitude. But design work does need stretches where taste can form before it becomes consensus. In a work culture that treats collaboration as a default good, that distinction matters.

This is also why the tools question matters. A portfolio system, a private reference space, even a type foundry site are not neutral containers. They either protect the conditions where taste can develop, or they pull the work back toward the defaults of the platform. Van Schneider’s career makes that feel less like a manifesto and more like a working habit: when the available environment makes the work smaller, build a different environment, then keep using it until it changes the work.

Van Schneider’s favorite advice turns complaint into output:

“The best way to complain is to create something” by James Murphy, founder of LCD Soundsystem. It has become one of my guiding principles. It turns useless, negative energy into productive, positive energy.

Design leaders spend a lot of time telling teams to experiment with AI. Nathan Lavertue, a Design Program Director for IBM Z and LinuxONE, turns that advice back on leadership itself:

We spend a lot of time helping designers understand how to work with AI. The question I keep coming back to is simpler. How many of us are doing the same for ourselves in ways that meaningfully support the business?

So instead of just encouraging my teams to experiment with AI tools, I put myself in the work. I built a design program signals website using IBM Bob. What started as a wireframe to sketch out an idea became something I realized I could actually build myself. That surprise is the whole story.

I appreciate the reciprocity here. If designers are being asked to work through this shift in public, leadership cannot treat AI as a strategy deck it reviews from a comfortable distance. You cannot build useful judgment about these tools by asking other people to absorb the uncertainty for you. That is why I’ve been reading about them, writing about them, and experimenting with them on my own. Whether it’s OpenClaw, Hermes, running a local LLM, ComfyUI, or Claude Design, curiosity is key here.

The interesting part of Lavertue’s example is not that he made a dashboard. Dashboards are cheap. The useful part is that he used AI to make a leadership problem legible enough to discuss. His signals site pulled together team health and business impact, then sorted indicators into required, expected, and optional categories so the absence of a signal became something to interpret, not just a blank cell to punish.

Lavertue is clear about this:

I had to remind myself of that more than once while building it. The signals site was useful. Bob was a capable collaborator. But the risk with any tool that comes together quickly is mistaking the build for the point. The site was never the outcome. It was infrastructure for conversations. Design’s impact on the business was the outcome. Keeping that distinction clear required the same discipline I would ask of any designer getting excited about a new tool.

The site did not replace leadership judgment. It grounded it. Instead of reacting to delayed updates or anecdotal signals, I could engage teams with shared context and a clearer ability to look forward rather than back. This was another form of walking the walk. Not just encouraging teams to work differently but building the system that made that work visible and meaningful.

That feels like the better bar for AI-native leadership. Not “leaders should code now.” Not “every management problem needs a custom tool.” The bar is whether leaders are willing to put their own work through the same change they are asking from their teams.

Fulya Lisa Neubert, writing for the Slack Design blog, starts with a familiar design handoff problem:

For most of my design career, Figma was where the real work happened. I’d design screens, build prototypes, then hand off the designs for someone else to build. If something felt slightly wrong in production, we’d go back and forth trying to articulate what “it should feel like” in words. This process worked well enough, but there was always a gap between what I could show in a static design tool and what someone would actually experience in the product.

The piece gets concrete when Neubert moves from screens to Slack search. She points to a kind of interaction where static prototypes can suggest the flow, but can’t prove whether the experience works under someone’s hands:

The search experience in Slack is a deeply keyboard-driven feature: typing states, focus management, the way content scrolls and reflows as you interact. These are things you can sketch in Figma and even prototype to a degree, but a Figma prototype can’t tell you whether the focus ring moves correctly between elements when you press tab, or whether a scrolling gradient feels right as content overflows. You need to actually use it.

I don’t read this as an argument that designers need to become frontend engineers. Neubert came in with the basics and figured it out:

I came into this with basic HTML and CSS — enough to roughly understand what I was looking at, but not enough to write it myself. That turned out to matter less than I expected. The first time I described a focus interaction in plain language and had a prototype working in minutes, I stopped thinking of code as someone else’s territory. […]

My setup is fairly straightforward. Cursor is my main environment. I use Figma’s MCP integration to pull in components I’ve already designed and Slack’s design tokens, so I don’t have to rebuild spacing, color, and type from scratch every time. I tried working directly in Slack’s codebase — years of accumulated complexity that made every small change feel like a bigger undertaking than it needed to be. […]

The Figma integration matters because the prototype isn’t starting from a blank toy environment. It pulls real components and tokens into a lighter workspace, which makes the thing shareable without pretending to be production. For Slack search, that means the team can review behavior instead of debating screenshots.

Neubert is also clear about the tax:

AI also doesn’t always preserve what you’ve already built. You prompt it to change one thing, and it quietly breaks something else. If you’re not testing after every turn, you won’t notice until you’re sharing the prototype with someone — or worse, presenting it — and that’s when you realize something’s broken.

That is the right caution. AI changes the distance between intent and working behavior, but it doesn’t remove verification. If anything, it makes the habit of testing after every small change part of the design process itself.

Dan Carey leads product at Anthropic Labs, the team behind Claude Code and Claude Design. In a talk on how a three-person team shipped Claude Design in ten weeks, he describes what happened to everyone else after their engineers got fast:

And so once Claude Code took off, the bottleneck moved. The bottleneck moved from building the feature to figuring out the right things to be building for your users, in a lot of cases. So the option was either skip those early steps, just try and decide on the fly, and potentially build the wrong thing really fast, or try to find ways for the rest of us to speed up. So our designers, our PMs, were having trouble keeping up. We needed our own accelerator tool.

Carey just relocated the bottleneck onto the exact work designers and PMs own: figuring out what’s worth building. That’s product discovery becoming the real constraint. When building gets cheap, what’s left to get right is the decision about what to build at all.

How does the team make that call? Not by writing it down:

So we like to use prototypes because documents are imprecise. It’s so easy for two people to look at the same doc and have two different products in mind about what the experience should be. […] Prototypes are more concrete, more visceral. They let you get hands on with the thing and really feel the experience yourself.

They skipped the PRD and the vision docs entirely. A working prototype immediately aligns people, and it doubles as the discovery tool: you build the rough thing to find out what the right thing is.

And it helped that the team was small enough to skip coordination entirely. Here’s Carey:

Everyone on the team does everything. The engineers talk to users, PMs write code, designers do data analysis. All of these things are enabled in part with Claude. And the lines between the roles on this team, they have essentially dissolved at this point. You do have your specialization, you do have the unique perspective and diversity that you bring to a team, but at any moment, any one of these people on this team can talk to 10 users, you can realize what the underlying problem is, you can design a solution to it, you can ship it to users, you can listen for feedback, you can keep iterating solo if you need to.

On Carey’s team, the designer who spots the problem also builds the solution and ships it. That’s the kind of role a lot of designers are now being asked to grow into, and it looks less like a handoff between specialists than one person carrying an idea from problem to finished screen.

Speed doesn’t guarantee you build the right thing, though, and Carey is candid about the team’s misses. They built a set of advanced, fine-grained controls for power users. A few vocal testers loved them—I know I would have. But the usage showed everyone else hated them, and the team pulled the controls in a week. Two lessons came out of it:

So this taught us a couple of things. One, this taught us that we should be a tool that lifts the level of craft for everybody, not just the ceiling on power users. It also taught us that we want to be as open as possible, because there will be users that we never meet the full needs of. There’s going to be some power user out there who wants to do something very specific that we’re not going to support. And that’s what convinced us that we wanted this to be a very open tool. That’s why if you export from it, you get HTML, CSS, JavaScript.

Addy Osmani makes a clean separation that most of the “is AI making us dumber” discourse keeps glossing over. He reports on Anthropic’s randomized trial of engineers learning a new Python library:

Engineers who used AI to ask conceptual questions scored above 65%. Engineers who copy-pasted the generated code scored under 40%. The tool didn’t determine the outcome. The posture did.

Osmani is writing for engineers, but most of that translates to designers picking up Figma Make, Lovable, or v0. Ship-without-comprehension scales beautifully right up until the moment you have to debug, redesign, or defend a choice you didn’t really make.

He ends on a ritual any designer can adopt verbatim:

I’ve started ending coding sessions with a simple question: did I learn anything today, or did I just close tickets? Sometimes the honest answer is “I just closed issues” and that’s fine. If it becomes the answer for months in a row, cognitive debt is accumulating in the background. Ship and learn are two separate metrics.

Workslop is the companion failure mode: the cost goes to your coworkers, where skipped learning costs your future self.

The artifact-to-intent argument has been working its way through design writing for a while now. What Jakob Nielsen adds to it, writing in UX Tigers, is a name for the failure mode that comes with the territory:

We used to accumulate design debt when teams shipped inconsistent components or patched over poor flows. Now we will accumulate intent debt: undocumented assumptions, vague brand guidance, missing escalation rules, untested agent permissions, and research insights that never become usable by the systems doing the work. Intent debt will be harder to see than visual inconsistency, but it will be more damaging because it compounds invisibly through every generated output.

Nielsen’s prior writing on intent-based UX argued that evaluation has become the new bottleneck for the user. A chat completes the task in seconds, and you spend the next half hour checking whether it actually did what you meant. Intent debt extends that bottleneck to the organization. The team ships ten variants in an afternoon, and nobody can tell which ones violated a brand rule that was never written down, or bypassed an escalation path that only lived in a senior designer’s head.

Nielsen puts the failure plainly:

The new danger is that AI will produce many adequate screens that all seem defensible in isolation and incoherent in aggregate. Mediocrity will arrive well-dressed. The designer’s role is to prevent the organization from drowning in plausible options.

Which is why the design system has to grow up:

The design system thus stops being a component library and becomes an operating system for taste. Tokens, components, and usage rules are only the visible layer. Underneath must be a deeper set of instructions about brand behavior, interaction philosophy, accessibility standards, motion logic, content tone, escalation patterns, and product judgment. The system must know not only which button to use, but when not to add a button at all.

Developer Mark Anthony Cianfrani has argued that LLMs finally let us ship the reasoning behind a token alongside the token. Nielsen draws the consequence of skipping that work: a weak design system in the AI era becomes an active liability. Agents will faithfully build with whatever’s encoded, and faithfully invent the rest.

Last week I linked Ravi Mehta on the three layers of context engineering for AI prototyping: functional spec, visual wireframe, structured data. Karo Zieminski, an AI PM writing Product with Attitude, makes the same case at the product scale and cites Mehta directly. Mehta wrote about prototyping one screen; Zieminski writes about designing the whole product around an agent.

Zieminski puts it in one line:

Prompt engineering is deciding what and how to ask the model. Context engineering is deciding what the model knows when it answers.

Then the asymmetry:

A well-crafted prompt in a poorly engineered context still fails. A poorly crafted prompt in a well-engineered context often succeeds.

That asymmetry is the argument for treating context as the underlying system.

If that asymmetry is real—and a year of using these tools tells me it is—then most teams are still optimizing the wrong layer. The visible artifact is the prompt. The work that actually decides the output is everything around it.

The piece I want to underline is who owns the work:

PMs define what goes in each context layer. Engineers build the infrastructure to fetch and store it… If the PM isn’t doing this, one of two things happens. Either an engineer makes the product decision by default, or nobody makes it and the agent gets every available signal dumped into the window.

Zieminski calls the alternative abdication. I think she’s right and I also think most PM job descriptions in 2026 haven’t caught up. The hiring filter still selects for ticket-shaping and roadmap maintenance, not for “decide what the model should know about the user, what should age out, what should never get re-fetched.” Those are product decisions about how memory is organized, and the people best positioned to make them—PMs who understand the product and the user—are often the ones least equipped to talk about retrieval and eviction. The gap is one of vocabulary and authority.

Both write for PMs, but the work is also design work. The context an agent sees is a designed surface: what gets included, what gets hidden, what should age out, what should persist between sessions. Mehta’s three-layer brief—spec, wireframe, JSON, twenty minutes in Figma, real data—is daily prototyping for designers working with agents now. Zieminski’s architecture is the system those prototypes live inside. If designers don’t show up here, PMs and engineers will design this surface for us.

Most agent-velocity hype rests on one premise: that writing code was the slow part. .txt, the team behind the structured-generation library, takes a saw to that assumption. The point goes back to two foundational software-engineering texts—Fred Brooks’s The Mythical Man-Month (1975) and Gerald Weinberg’s The Psychology of Computer Programming (1971)—and .txt puts it like this:

Software is what’s left over after a group of humans finishes negotiating with each other about what the system should do. The code matters, but it is the residue of the harder work, not the work itself.

Code as residue. That inversion reorganizes the whole conversation. The tools and processes we’ve built around software for fifty years—IDEs, wireframes, mockups, code review, even pair programming—have been about lowering the cost of producing the residue. Once that cost approaches zero, what’s left to slow you down is the negotiation underneath. And that negotiation has not gotten any cheaper.

What that layer actually consists of, in practice:

What slows down a team where agents do the implementation is the production of specifications precise enough for an agent to pick up and run. Roadmap, written down. Acceptance criteria, written down. The “what we actually want” forced into precision, be it via a test suite, a ticket, or a written design.

The bottleneck moves from people writing code to people deciding what code should exist. .txt calls that work management, and I’d put it a little wider; it’s also product, design, and anyone whose job description includes the phrase “what we’re building.” A spec precise enough for an agent is a falsifiable description of the outcome, with the trade-offs already made.

.txt on what runs underneath the spec:

Context is the commodity an organization runs on. It is the shared understanding of what we are building, why it matters, what has been tried, who decided what, what is load-bearing and what is vestigial. Humans on a team accrete it by osmosis. By being in the room, by reading the same Slack channel, by debugging the same outage at two in the morning. Most of it is never written down. When a senior engineer reviews a PR and says “this’ll break the migration,” they are drawing on context that has no document. Agents cannot do osmosis.

“Agents cannot do osmosis” is the line. Specs are the formal surface; context is what’s underneath, and teams absorb it without writing it down. The post closes here:

The companies that win the next decade will not necessarily have the best models or the best agent infrastructure. It will be the companies whose fifty people, then two hundred, then two thousand, can stay aligned on a shrinking set of decisions while shipping more output per head. They will be the ones that already knew, before agents arrived, that their hardest problem was coherence. That is a culture and management problem. Always has been.

Jess Eddy reaches back to the 19th-century pessimist Arthur Schopenhauer for the distinction senior creatives may worry about:

Talent is like the marksman who hits a target which others cannot reach; genius is like the marksman who hits a target which others cannot even see.

Eddy borrows a line from Jack Grapes, the poet and writing teacher: “Make me look good, and I’ll keep you on the payroll.” That’s the trap. The longer you’ve been at it, the more reliably your talent delivers, and the more expensive it gets to walk away from what works. Most career advice says lean into your strengths. Eddy says your strengths keep you aimed at targets you already know how to hit.

For experienced designers, those targets are getting harder to find. AI is changing what counts as design work and what tools do it, and the ground under the profession is moving with it. The reflex when the ground moves is to double down on the move you’ve already mastered. But the mastered move hits the visible target. The targets that come next won’t be visible yet.

Eddy doesn’t let anyone skip the mastery step. The 5–10-year window isn’t optional. But once you’ve put in the time, you have to walk away from the talent that made you reliable. Eddy closes with Grapes:

Talent does what it can, genius does what it must.

Nearly nine in ten organizations now use AI in at least one business function. Ninety-four percent aren’t seeing significant value from it. Gale Robins, writing for UX Collective, argues that the gap is a framing problem, not an adoption problem. Her earlier piece on discovery judgment made the same case; the new one sharpens it with an anecdote that shows the trap:

A team I spoke with recently had compressed their discovery cycle from six weeks to ten days using AI. They were proud, and the throughput was real. When I asked what the work had taught them that they did not already believe, the answer was: not much. Same questions, faster. Same answers, sooner.

Same questions, faster. Same answers, sooner. Her analogy for the wider pattern is the electric factory one I’ve used before:

When factories first installed electricity, productivity barely moved. Manufacturers replaced steam engines with electric motors and kept the line-shaft layout. The breakthrough came later, when they redesigned the factory around what electricity made possible. The technology was only part of the answer.

Robins maps McKinsey’s three waves of AI value—productivity, differentiation, transaction-cost reduction—and finds most teams stuck in the first one. Robins on where they have to go to get out:

These decisions are upstream of every artifact a team produces. They are also where AI productivity gains help least, and where human judgment compounds the most.

Robins’s evidence undersells her own thesis. She leans on Generative AI at Work—the Stanford-and-MIT customer-support study by economists Erik Brynjolfsson, Danielle Li, and Lindsey Raymond that became the canonical citation for “AI helps novices most”—to argue AI raises the floor, not the ceiling. Novices gained 34%; experienced workers, basically zero. That’s why so many designers who have never coded—like me—are now suddenly shipping with this newfound superpower. It’s the same finding behind the junior designer crisis. But LinkedIn’s Full Stack Builder rollout found the opposite: top performers adopted AI fastest and got the most out of it, because they had the judgment to know what to ask for. The floor-not-ceiling story is only true where the questions are fixed. Once the questions are the work, the pattern inverts. That’s exactly the territory Robins is mapping. If AI rewards the experienced most when the work is judgment-shaped, framing is where the gap between teams widens.