466 posts tagged with “ai”

Software developer raf, who learned the craft by building and breaking web applications, describes a new apprenticeship problem that starts with an apparent success: a beginner can generate competent work on day one.

I’ll tell you this much: it’s not from consuming good work. You cannot read a hundred excellent programs and absorb the judgement by osmosis, any more than you can become a chef by eating in good restaurants. Taste is built the slow, stupid, humiliating way: you make something bad, you are forced to live with it, it fails in front of you, and some part of you files the failure away. Then you do it again. The palate is an accretion of your own mistakes, sat with long enough to sting.

The friction was not an obstacle to developing taste. The friction was the curriculum. Every wall I cursed while climbing it was, without my noticing, teaching me which walls were worth climbing. The cost that rationed my output also educated my judgement, because paying the cost over and over is how you learn what is worth paying for.

For designers, the old bottleneck did two jobs at once. Producing a weak concept, defending it in critique, watching it fail with users, and revising it made bad choices costly enough to remember. AI can compress that cycle into a polished mockup before a beginner has learned what to reject.

raf on the cost of taste:

This is the quiet cruelty of the situation and I do not have a comforting way to phrase it. Taste is slow. It says “no, again.” It sends the plausible thing back because plausible is not the same as right, and while it is doing that, the person without it has already shipped, closed the ticket, and moved on. The market timed you both with the same stopwatch and it did not see the difference. It cannot see the difference. Taste does not show up in the diff.

That makes critique more important, not less. Design teams need to value frank feedback from the skeptics in the room, not only the ones who ship fast.

Adam Waxman built five personal tools using his household’s rules and data from several apps. Most work the same way:

Aggregate data, add an LLM. The cost drop doesn’t just mean more apps, it means apps can be far more personal. Most of mine follow the same shape: pull data from multiple sources, combine it in one place, and use an LLM to generate insights from the full picture. My fitness app combines nutrition, sleep, weight, and training data that lives in four separate apps, then uses that context to make recommendations none of them could alone. I think this pattern will spread to professional tools too.

Two more lessons:

Ephemeral is fine. We used the sleep app for about four months. Our son sleeps through the night now, so we retired it. If the app had taken me months to build, that might sting. Because it took a week, I’m just glad it worked when we needed it.

AI floods big markets and unlocks small ones. Yes, AI produces endless derivative apps. But the same tools also let me build apps for my household that no company would bother making. Beyond my household, I built nycjazz.guide for NYC jazz fans and claudecodedaily.com for Claude Code developers, audiences too small for a business but worth serving.

These fleeting micro-apps change what is worth designing. An app no longer needs a large market or a long life to be useful. They also avoid the customer adoption problem Andy Budd describes: Waxman starts with one household and one problem, not a market he has to win over.

These personal apps depend on other products letting people use their data:

Good APIs matter more than ever. I switched from Cronometer to FatSecret because FatSecret had a better API. My fitness app pulls from Strava, Oura, Withings, and FatSecret, and the quality of each integration depends on how well the API is designed. As more people build personal software, users will expect their apps to have APIs and MCPs worth connecting to.

Designers now need to ask whether people can get their data out. If a service keeps that data locked inside, people can’t use it to build tools that fit their own lives.

Joost de Valk, creator of the Yoast SEO plugin for WordPress, has a better architectural answer to separate sites for agents: maintain one site with several interfaces.

Before you rush off to build a separate site for agents: we’ve done that before. Remember m-dot sites? Remember AMP? Parallel versions of your website always drift out of sync, and then they die. The answer isn’t a second website. It’s one source of truth with multiple renderings, where the machine-friendly surface is generated from the same content the human one is.

For website owners, keeping the publishing system accurate matters more than polishing any one rendering. For designers, the concrete consequence is to define the content model and available actions at the system level, then test every interface against the same rules. De Valk on what those interfaces need to support:

A website used to be an HTML thing, in some cases with an API. That’s no longer enough. If your website matters to you, it should at the very least also be an MCP server, and in many cases even ship a command-line client. That way agents can interact with your business directly: check availability, place the order, book the slot.

The specific standards will churn. MCP is today’s answer; something else will come after it. The durable point is this: a website is becoming a bundle of interfaces. One for humans, several for machines, and the machine ones don’t just answer questions, they take actions.

More and more buying decisions happen away from your site: in AI answers, in chat interfaces, in agent-mediated transactions. This is the uncomfortable one. You can rage against it or you can plan for it.

The website doesn’t die in that world. Its role shifts. It becomes the canonical source of truth about your business: the system of record that feeds every surface where the actual decision gets made. If an AI assistant recommends you, it’s because your home base gave it every reason to. Thin, stale, machine-hostile websites simply won’t be recommended.

Arin Bhowmick, SAP’s chief design officer, shifts the software-death debate from apps to operating context. Rather than treating the model’s capabilities as the whole story, he asks where enterprise software’s enduring value actually sits:

Despite the cliché premise, I found it insightful, an honest take from one of the “Big Four” on AI and SaaS. In it, PwC’s TMT leader Dallas Dolen says enterprise software isn’t dead. His clients are redirecting their software budgets toward AI, and the systems going in on top of the stack they already run, keeping what runs the business and building the intelligence on top. They are betting that the value sits in what they have spent years assembling.

There’s a good reason for that. An AI model can answer almost anything, but on its own, it knows nothing about your company: how it runs, which data to trust, what it is allowed to touch…

The model is the “cheap” part, while the expensive one is the business knowledge around it, the processes, exceptions and rules a company encodes over decades. That context provided by software is what makes an AI agent useful.

The existing stack becomes the policy layer for whatever intelligence sits above it. That context determines more than what the agent knows. It sets the boundaries of action: when the system can proceed, when a person must approve, what happens when the evidence conflicts, and who is accountable afterward. Bhowmick turns those boundaries into the design brief:

Once design “owns” the judgment a system applies on its own, a new set of questions lands on every designer’s desk:

• When should an agent act?
• When should it stop and ask first?
• What does it show before it does something it can’t undo?
• When it isn’t sure, does it say so, or hand you a confident guess?
• When it gets something wrong, who answers for it?
• How does a person take back control when they need to?

Often, the most useful move an agent can make is to admit when it’s unsure and hand the decision back. We have to design that handoff so it feels natural. If it reads as the software confessing a mistake, people lose trust.

I’ve started asking Claude Design for multiple directions on a canvas, then sending the promising ones through another pass before I choose. In a conversation between Michael Riddering and designer/builder Kyle Zantos on Dive Club, Zantos builds the missing part of that workflow: an interface for reviewing what the agents made.

Zantos uses a standalone HTML artifact to render the proposals and collect decisions in the same place:

You act as the orchestrator and the quality-control reviewer and send out Opus 4.8 sub agents to execute these audits as each skill does it. Compile the results, but at the end of it, compile it all, remove the duplicates, and then present me an HTML doc that has all the proposed changes and then options for me to approve, deny, or discuss them, and then have a way at the bottom to copy all of what I put in here and just paste it right back into my session with Claude.

Zantos on combining decisions across different parts of a product:

Then be like, “Oh, no, not that. That’s terrible. I hate that.” Or just like, “Yes, but add this other thing.” Or, “I really like what you did in number two on number six that’s in a totally different area of the app or the site or whatever that I’m working on, but grab the technique that we used in the second point of this doc.” So just visualizing everything from your agents has been huge.

Product designer and educator Aliena Cai argues that AI is changing the work, but isn’t replacing one kind of product designer with another. I think her survey shows that replacement can happen without cutting jobs:

The survey found that 60% of design leaders actually expect to keep or grow design headcount. Only 10% of design leaders expect to reduce design headcount, and 8% of design leaders say they are shifting investment towards hybrid roles like design engineers. However, expectations for design talent have shifted. 50% of design leaders are looking for a greater emphasis on AI tool fluency when it comes to hiring. And 48% of design leaders are placing greater emphasis on systems thinking and strategic skills. I would just call it business sense or product sense.

If you’re a manager, you may have heard the terms “rotate people out” and “upskill the role.” That might be happening here. The number of design jobs may stay the same, but the requirements are changing. Experienced designers will be asked to use AI, think about the wider system, and understand the business. If they don’t, they’ll be managed out. People entering the field will face that higher bar from the start. Some who would have qualified for the old job won’t qualify for the new one.

AI fluency here means using the tools to make and revise working prototypes. Designers still need to understand the user and business problem, check what the tools produce, and decide what should ship. Cai gives similar advice:

Based on everything I shared today, I think that if you are an experienced product designer, then there are two things you can do right now to secure your career for the future. Number one is to improve AI fluency. And number two is improve your business sense. But AI fluency really doesn’t mean just using AI for everything, because AI, at the end of the day, is an amplification tool, and it amplifies your design skill and product sense. Just like I said, it brings great ideas to life faster, but it can also bring bad ideas to life faster.

I couldn’t agree more with the AI fluency point. That’s why I’ve been reading and writing about it for over a year.

Podcaster Lenny Rachitsky interviewed Ian Silber, OpenAI’s head of product design.

Ian Silber discusses task-specific UIs. He shows how ChatGPT’s transcript can make room for them:

Another good example is, then once you actually ask it to do something, the chat transcript doesn’t need to be purely text, right? We’ve seen these now evolve so far from what it was with when ChatGPT just launched. It’s no longer just a pure chat bot.

It actually has many features within that, right? So a good example is what we call writing blocks. You ask it to write you a letter or an email.

And it now contains that email in a block that you can directly manipulate, and you can type with it, and you can brainstorm, and you can edit little bits and parts of it. You can copy just that. And that has made that interaction much better.

And we’re seeing the same thing with image generation and creating special tools for that kind of mode. And I think that’s where the UI is going to start to really continue to evolve. How do we make if you come at this and you’re a designer? Maybe ChatGPT should actually really look different or have different affordances. Or you’re a data scientist or you’re using this to run your home, your your personal life.

Maybe it should really kind of work or look differently.

Chat is useful at the beginning of an interaction because users can express ambiguous intent in plain language. The UI can then provide task-specific controls. These might be a writing surface for an email or image controls for a picture. A designer might need another set of affordances. Silber’s example turns the post-chat argument into a product architecture already appearing inside chat. In other words, generative UI.

Chat usually collapses two jobs: understanding what someone wants and giving them the right environment to finish it. Silber on how one entry point could handle both:

And hopefully all of this gets simpler as we do it too, right? When we think of the super app, I think eventually we really think of the idea of especially for our broad consumer base. We do think that there should be this one universal input that you can ask that knows when it needs to answer really quickly, or it should be chatty, or whether it should go off and do work for you.

And so, that’s a big excitement that we can get to. How can we continue to simplify the experience such that you shouldn’t have to think about how this works. You shouldn’t have to think about what model you’re on or what sort of speed you’re using. That goes back to the balancing the different kind of personas and user needs. Of course we want to expose that for the right people, but then we want to make sure that we can get a default experience that works great for for most people.

Quotes slightly edited for clarity.

In an interview with Ryan Mather, Federico Villa, and Robin Chen, OpenAI product designer Benjamin Zweig explains why designing memory for ChatGPT required studying how people remember. The work moved beyond the UI. Zweig had to turn invisible human judgment into training examples and evaluation rules that would shape the model’s behavior:

And the core of it all really is, how do you turn qualitative decisions into a quantifiable rubric? That’s exactly how you train a model to get better at any task.

Here, this meant formalizing the very human skill of determining what’s worth remembering, both in terms of what you save and when you recall it. If you ask a person to explain how they make that decision, they’re not going to have a clear answer for you. It’s an inherently human thing. So what you do is you look at a bunch of examples, apply your human intuition to make a decision, and then look for patterns and ways to turn that intuition into a heuristic that you can train against. That’s a new skill for many designers, and a new way of thinking and working.

Organizations often miss work their metrics were not built to see. AI agents face a similar problem when they encounter something their rules cannot handle. Service designer and University of Michigan urban-technology professor Ron Bronson turns to Stafford Beer’s idea of algedonic signaling: a way to report an exception outside the usual channels.

One reply to the thread referenced algedonic signalling, which sent me back to Stafford Beer.

In the Viable System Model, an algedonic signal is an exception channel: information that can move outside the normal reporting structure when actual conditions have departed badly enough from what the system expects. Beer wasn’t thinking about “agent welfare” and to be honest, neither am I. At least it relates to anthropamorizing an agent’s welfare, when in reality what i care about exclusively is what gets done well, what’s gets done successfully and ensuring an agent stays within the boundaries of its remit. So, the usefulness of the idea here doesn’t depend on deciding whether a model experiences anything like pain because that’s stupid and makes me angry, even as a suggestion.

But a system that operates with some autonomy needs a way to tell the rest of the system when ordinary control is no longer adequate.

Bronson separates two jobs. A judgment router decides whether an agent is allowed to act. An exception channel gives the agent somewhere to report a situation the router does not understand.

An agent can be completely authorized to perform the task in front of it and still encounter something it doesn’t know what to do with. The API returns 200, but the answers contradict each other. A tool works exactly as documented while exposing some behavior that looks dangerous, or the task can be completed but the agent finds something else that seems wrong and goes on a goose hunt.. Nothing has necessarily crossed an authorization threshold because none of this is within the original parameters, but it’s also not outside of them.

That makes the exit door a different piece of infrastructure from the gate.

An alert matters only if the system responds. Bronson’s diagram shows that it can let the work continue, stop the task, or change its rules for next time. Otherwise, it has only recorded the problem.

The author of the thread built a small pipe from an agent to Teams and called it distress_call. and the key is this, there should be somewhere for the agent to go when it needs to alert something is off.

The judgment router still matters because somebody eventually has to decide what an agent is authorized to do, when its discretion ends and where accountability sits, but the exit door takes that concept a step further and helps us track the inconsistencies, and perhaps the errors to better spot problems that might consequential.

For Agent Experience, that expands the design surface considerably. Making an environment easy for an agent to operate is only part of the job. We also have to decide how an agent stops.

The system learns only if the alert changes what happens next time. The next agent should stop sooner or call the right person.

Have you ever walked into a fast food restaurant lit with overhead fluorescent lights and knew immediately that it might be a bad idea? Or if you walk into a slightly more upscale eatery with warm, incandescent light and it immediately feels inviting?

Creative technologist Jem Gold illustrates a similar phenomenon in UI design. There are mechanical specs about pixel values and hex colors. And there are guidelines for the experiential quality of the product.

Good design absolutely talks about feeling, atmosphere, taste, mood, audience, pacing, the embodied experience of arrival. But that work gets compressed too early into machine-readable implementation language. The moment a design intent becomes a token, the temperature drops out of it.

I keep coming back to this distinction:

Object-language describes the artifact. Warm neutral palette. Elegant serif typography. Subtle texture. Soft cards. Generous spacing.

Encounter-language describes the experience of meeting the artifact. Stepping out of your car into a eucalyptus grove. The taste of the pacific air in Mendocino. Golden hour sunshine kissing your forehead. Copal smoke wafting through a mountain cabin. The page has already exhaled before you arrive. A room prepared for attention. Clarity without sterility. Intellect without deadness.

The second kind of language contains more design direction. It carries sensation, mood, pacing, social meaning, and implied structure all at once. A single sensory scene can encode color, spacing, contrast, materiality, ornament, and density without naming any of those directly.

It is precise along a different axis.

That difference becomes real once models can turn atmosphere into visual form. Encounter-language gives the model a clearer target, but you still need taste to decide whether the result feels right. Gold extends the argument from direction to judgment:

The model generates possibilities, but taste remains in the loop as judgment. Does this still feel like the intent, or did it collapse into aesthetic costume? Only after a design tastes right do you derive tokens, typography scales, spacing rules, and component behavior. If our design specs only teach the model what the interface is made of, they have already forgotten what the interface is for.

On a morning train, Aaron Horwath, writing in Noema about AI and knowledge work, watched a fellow commuter spend over half an hour talking through financial metrics, including margins and cost structures. Then the man pulled a pair of knitting needles from his bag. He was making a pink winter hat for his niece, and Horwath noticed a glint of pride and excitement.

The hat was more than an escape from work. It was tangible, made by his own hands for someone he knew. That contrast gives Horwath’s essay its real subject. AI job anxiety is the backdrop; the deeper rupture comes when abstract corporate work loses its remaining sense of participation and ownership.

Knowledge work’s one saving grace, until recently, was that it was still executed by humans. We were needed. It was flesh-and-blood humans who sat down to work through a challenge, built the slide deck, wrote the customer response and developed the strategy. Even if it was existentially meaningless, there was human thought, collaborative work and creativity poured into that work, giving it life.

Now, AI agents are increasingly executing much of that work for Knowledge Workers. It is common for people responsible for integrating these tools into their organizations, myself included, to describe the future of work as one in which all humans will essentially be managers of armies of AI agents. That seems pretty great. Let the software compile the reports, chase down the data, format the deck, draft the first pass of documentation and handle the dozens of small, repetitive tasks that used to quietly eat an afternoon. But in many cases, employees under pressure from leaders to produce more are using AI agents for far more than grunt work: formulating complete business strategies, generating full marketing campaigns, building entire websites, drafting strategy for whole divisions of an organization. From a single email to an entire corporate strategy, the outputs of individuals, teams and organizations are increasingly generated in an instant by AI.

Horwath distinguishes between using software to remove drudgery and using it to remove the process through which people learn together and take responsibility for what they make. In design, critique and mentoring can look like bureaucracy, even though they are how designers develop judgment and ownership of a decision.

Research suggests this group needs that environment to do their best work. Harvard Business School’s Teresa Amabile spent decades studying what produces genuinely creative, high-quality output. Her Intrinsic Motivation Principle determines that people do their most creative and innovative work when motivated by the work itself — the interest, the challenge, the enjoyment — not by outcomes or metrics. The environments that kill creativity are political, risk-averse and relentlessly outcome-focused. The environments that stimulate it are collaborative, idea-driven and free. The messy middle, in other words, isn’t inefficient. It’s the condition under which genuinely valuable work gets produced.

It’s important to note that the impact of removing the messy middle from the work experience of these two groups is asymmetrical: For outcome-first people, it is a victory. For experience-first people, it undermines the foundation of work.

The messy middle carries more than delay. It is where people argue, teach, notice, revise, and become accountable to one another. Automating production can be a gift. Automating away every encounter that nurtures ownership may push workers away.

The knitted pink hat reveals what efficiency metrics miss: people care about work they can recognize as their own and connect to another person. For design teams, the risk is faster production with fewer chances to develop judgment together.

Dan Maccarone, product strategist and co-founder of the design studio Charming Robot, draws a line between what expertise can be documented and what must still be learned through first-hand experience:

The move nobody makes: run both plays on purpose. Structure what you can, and still teach it to a person.

The trap is thinking you have to choose between them like my co-founder clients. But you don’t. The better move is to run both plays at once, on purpose: write down everything about your judgment that will survive being written down, and build the apprenticeship for everything that won’t.

Start with the writing-down, because there’s a catch everyone hits. Getting your judgment out of your head isn’t the same as dumping your files into Claude. Do that and you get mush. The model grabs the loudest thing you’ve ever said and hands it back with total confidence.

In design, AI guardrails can store the reasoning, conditions, and exceptions behind a product decision. Apprenticeship gives people decisions in context through critique and design reviews, where correction becomes pattern recognition. Reading the expert’s answer is not the same as learning how to arrive at one.

Trust it like you’d trust a very sharp new hire who’s read everything and lived nothing: useful, fast, and occasionally, cheerfully, wrong.

Maccarone’s prescription is to document the method and pair it with apprenticeship, so people learn when to use their own judgement.

Andy Budd, Clearleft co-founder and longtime design leader, argues that AI may make design organizations smaller while making some design roles larger. His bull case is that designers who can move across product, craft, commerce, and implementation gain agency instead of getting stuck in production hell.

The best designers start to look less like traditional product designers and more like hybrid product leaders. They still care about interaction, hierarchy, language, flow, brand and craft, but they also understand the commercial shape of the problem. They can make trade-offs. They can prototype in code, or close enough to code. They can use AI to explore options quickly, then use judgment to throw most of them away. They can sit with a founder or PM and move from a vague product concern to something tangible by the end of the day.

Budd on the bear case and the danger of plausible design:

The problem is not that these people will suddenly become great designers. The problem is that many companies do not know the difference between great design and plausible design. Plausible design is dangerous. It looks coherent in a product review. It uses the right components. The spacing is fine. The copy is not embarrassing. The flow mostly works. Nobody in the meeting feels strongly enough to object. So it ships.

Budd’s point is that these outcomes can arrive together: cheaper execution can expand the role of designers while giving companies that never valued design a faster route to mediocrity.

Matt Shumer, writer of the viral “Something Big Is Happening” essay, argues that most agent workflows try to prevent mistakes by specifying every move. His Gauntlet Loop takes a different approach: define what good looks like, then let the agents decide how to reach it.

You give a lead agent a goal and a real example of what great looks like. The lead agent decides how to break the goal into the smallest pieces that can be improved separately. Each piece gets its own builder and a separate critic with fresh context.

The builder makes something. The critic compares it against the reference example. If the reference example wins, the critic explains the biggest remaining gap and sends the work back to the builder. The builder fixes it. Then another round begins.

That continues until the result reaches the bar (or, more likely, you decide it is ready).

The agents get specificity about the outcome, not a recipe for producing it. On a design team, one agent could decide how to build a prototype while a fresh critique agent—Shumer calls them critics—compares the result with a reference and an explicit quality bar. Shumer separates the critic so freedom in execution does not weaken the judgment:

The builder and critic should be separate agents.

The builder has seen every decision it made. It remembers why it made them. That makes it very good at explaining why its work is reasonable.

You do not want reasonable. You want an independent judgment.

Spawn a fresh critic and give it the goal, the bar, the relevant rules, and the actual artifact. Do not give it the builder’s history or explanation.

Smarter agents can work with fewer procedural rules when their output remains inspectable. Shumer puts it into practice with a goal, reference, artifact, and independent review.

I’m am a pretty big Star Trek fan. As any good GenXer, I grew up with Star Wars, but I loved and still love the Star Trek universe. It just tickles the nerdier side of my brain.

I love this essay by Patrick Neeman exploring how Star Trek correctly predicted aspects of modern AI.

Neeman:

Star Trek’s track record is a design brief, not a trivia game. The lesson is not that the writers were psychic. It is that they reasoned about interaction and consequence, and those reason forward better than hardware guesses do.

• Design the conversation before the capability. The Trek computer worked as an idea because the interaction model was sound long before the intelligence was. Get the way people ask and receive right first, and let the underlying capability catch up to a surface that already makes sense.
• Treat plain language as a real interface, with real failure states. Natural-language input is not a magic layer that removes design work; it moves the work to disambiguation, confirmation, and recovery. Design what happens when the system mishears as carefully as when it hears.
• Show your seams on translation and transcription by design. These systems are mostly right, which is the dangerous kind of wrong. Surface confidence, make correction cheap, and never present a probabilistic output as settled fact.
• Put the consequence questions in the review, not the retro. If a feature optimizes for a metric, ask early what it does when it optimizes too well. The show’s warnings all came from goals specified narrowly and pursued faithfully.

“Plain language as interface” is happening all around us. We are now coding by using plain language. We’re designing using plain language. The chatbox has been derided as a terrible interface for AI by skeptical designers, including myself. But I think it has staying power precisely because of its support for plain language.

In Jason Lemkin’s account of SaaStr’s break with Adobe Marketo, the formal cancellation is almost beside the point. SaaStr had already moved most of Marketo’s work elsewhere:

And once you do that once, you start asking the obvious question. We’ve now migrated almost all of Marketo’s functionality onto Salesforce. Not because Salesforce has better features. Because Salesforce has an API our agents can actually use. One day you look up and realize you’re paying $60K a year for a product you’ve already routed around.

That is the new B2B churn motion. It isn’t a feature bake-off. It’s an agent-operability test, and legacy vendors are failing it quietly, one migrated workflow at a time, before they ever show up on the churn report.

I do think this is where a lot of software, especially B2B software is headed. Even in my own day-to-day, I’m loathe to use tools that don’t allow me to connect to one of my AI agents. Zoom out to an enterprise context and now agent operability becomes a product-quality problem that can eventually lead to churn.

Lemkin’s advice:

If you build or run a B2B company, the Marketo story is a checklist of what not to become.

Make your product operable by an agent, now. Not next year. The question is no longer “is our UI good.” It’s “can a customer’s agent do real work against our API without a human babysitting it.” If the answer is no, you are already on a churn clock you can’t see. Webhooks, an SDK, an MCP server, clean auth, real history. This is table stakes in 2026.

Judgment and craft may help a designer survive today’s freelance market. But Madeleine Morley, writing for Fast Company, gives us little reason to believe that either would guarantee economic security.

She interviewed Cari Sekendur, founder of the independent branding and web-design studio Butter.

In Fast Company ’s report, more than half of designers surveyed believe AI is undercutting their rates, and 68% believe it is eroding the value of human creativity.

“Clients now expect more while paying less,” wrote one survey respondent on AI’s effect on the industry. “Many clients are taking advantage of AI to underestimate our professionalism and pricing,” said another.

Morley writes that only 22% of survey respondents reported losing work because of AI. But the lowish number hides a sense that “AI isn’t replacing design, but it is changing how clients perceive it.” A designer can keep getting hired and still lose leverage over what the work pays and demands.

That pressure isn’t distributed evenly. She describes what has happened among illustrators doing smaller jobs:

Eric Carter, an illustrator whose clients include The New York Times, believes the disruption has been most acute at the lower end of the market. “There is this whole gig economy of illustrators doing smaller jobs,” he says from his studio in Los Angeles. “Fiverr was already cheapening everything… the bottom was already pretty low. And then having AI replace that, it just cut it out.”

Carter has been insulated from that shift because, as he puts it, “I get paid to think.” This idea—that a designer’s value is increasingly about their taste and approach as opposed to pure craft or technical ability—reverberates throughout the freelance industry. “You can’t automate taste and creativity,” one survey respondent wrote. Another: “Ideas and taste is what is needed to succeed.” And another: “AI is only as good as the strategy and input.”

Carter’s “I get paid to think” is encouraging, but it comes with a nasty condition. Judgment and recognizable craft offer more protection when clients already know whose judgment they’re buying. I wrote about the same trap in my series on the design talent crisis: entry-level work is how designers develop and prove their judgment, yet it’s the first work the market is stripping away.

Morley concludes:

What is evident across the survey data and multiple interviewers is that the labor of design is increasingly fragmented in today’s creative industry. Designers are forced to sell their labor in multiple markets simply to maintain the same standard of living that they used to and the market is not compensating highly skilled creative work in the way it did even a few years ago.

All of this raises bigger questions. For many years now, the freelance creative has been not only someone who simply sells their craft, but an entrepreneur expected to market themselves and build a personal brand. Diversification and resilience are emerging as absolutely vital and central capacities for freelancers navigating the age of AI. What gets lost in the urgent need to find sources of income and to adapt are questions like: Shouldn’t clients pay more? Why are corporations cutting budgets? The burden, as always, shifts to the individual.

Intent is one of my go-to coding workspaces. By default, it’s spec-driven and uses swarms of agents to execute the work, with a brainy orchestrator agent coordinating it all.

Ryan Mather, interviewing the product lead for Intent, designer-developer Amelia Wattenberger for AI Design Field Guide, asks how she designs when software behavior itself can change:

It’s a new dimension in which apps can be dynamic. We had image maps exported from Photoshop with a certain resolution, and then we were like, “wait, screens are going to be different sizes — this isn’t going to work anymore.” So we needed layout algorithms. Then users could interact and change what’s on the page — so we made them dynamic through time. Users had preferences — so we made them dynamic based on the user. And now there’s an additional dimension where even the app behavior itself can be completely changed - custom to you.

So designing now is a dance of, what are the primitives to add to the structure? Are we using bricks or another building block? And then the user can fully configure the app’s behavior, which I think is the only way to build a product now. Otherwise it’ll be stale in five months. Someone else will build a new app that does something around the workflows that people want.

Stable application chrome can provide continuity. In configurable software, Wattenberger locates that continuity in the objects people use to compose a workflow: the workspace that contains a task, the spec that humans and agents edit, and the subtasks that make delegation visible.

She describes what happens when those objects disappear:

I think about a straw that’s really bendy, and someone trying to do a thing with it — it’s just not a very powerful thing. Or clay — you can’t build super big things with clay, and you can’t go really quickly, because it’s infinitely moldable. Anything that’s generic is the jack of all trades, master of none. If you want something really powerful, you need some structure there. You need bones, you need muscles, some affordances. You can’t just be like, “oh, it’s a pile of goop, and stuff arises from the goop, that’s not what we all want.”

Mather then asks how far configurability can go. Wattenberger:

I have a feeling that applications as a paradigm will not last much longer, at least as the main way we interact with computers. Things will be way more modular and definitely decoupled from the data. There will also be a strong remix culture, because people don’t want to create everything they want from scratch, but they also want the ability to customize the things they use. So things are going to get super wacky.

OpenDoc, for example, imagined documents as containers for live, editable components supplied by different tools, rather than files owned by a single monolithic application. Computer scientists have been chasing variations of this modular model for years. AI, and the capabilities it gives users, adds a new dimension: the behavior of the application itself can now be remixed.

Paul Bakaus says he helped turn Impeccable from what he calls a “vibed personal skill” into a design toolkit. He says hundreds of thousands of designers and developers now use it. Three of his techniques show how much of that improvement came from engineering around model behavior instead of polishing a prompt.

The first is “Make them argue · two blind reviewers beat one confident guess”:

For a concrete example, /impeccable critique, Impeccable’s design review command, runs both deterministic checks against your code (to detect stuff like low contrast quickly and decisively) and reviews the design like a human would. But early versions had a major flaw that produced bad critiques: hand the LLM the detector output up front and it skews BOTH ways:

1. detector noisy → condemns a strong page over fixable nits (false alarm)
2. detector silent → rubber-stamps a generic page (meaningless clean bill)

To debias, critique now spawns two sub-agents that never see each other’s work: A = an LLM design director (hierarchy, slop, heuristics), B = the deterministic detector + browser evidence. When the sub-agents finish, the main agent synthesizes (weave, never concatenate) the results from both for a balanced critique. Two blind opinions beat one confident guess.

I agree with the architecture. Review independence matters as much for agents as it does for human teams; the reviewer needs a perspective the maker didn’t already shape.

Bakaus’s second technique is “Force divergence · escape the cluster, don’t chase it with bans”:

Bans just move the model from one region of latent space into another. The escape is sideways, not a longer denylist.

Here are three techniques that produce higher divergence (cheapest but weakest first):

1. Shave the safe picks: make the model name its top 3 fonts, then discard all three. Out-rank yourself.
2. Generate ~50, a blind sub-agent keeps the 5 most distinct. (Lives in Radiant’s codebase, not Impeccable yet.)
3. Seed from outside the model: palette.mjs --from 8f2a starts in a region the model never picks on its own.

This is a better response to repetitive AI aesthetics than accumulating another denylist of fonts and visual effects. The intervention changes the candidate set instead of pleading for a better choice.

The third is “Give them memory · runs that compound instead of restarting”:

Most skill authors treat skills as stateless prompts, but there’s no rule that says a skill can’t have state. Keep in mind that skills can be bundled with scripts that can be run anywhere during the lifecycle of the skill!

Impeccable’s critique and polish commands use this technique quite effectively:

1. /critique writes a snapshot per target (score, P0/P1, markdown + frontmatter)
2. /polish reads the latest snapshot as its fix backlog

One non-obvious detail that makes this work across a team: the snapshot slug comes from the resolved file path, not from how you phrased the request. A teammate pointing at the same file inherits the same memory, and the score trend (24, then 28, then 32) survives across sessions.

This one is immediately practical: save the work on disk, then let the next command pick it up.

If you’re configuring agents for product and design work, start by cleaning up their context. Anthropic’s Thariq Shihipar offers several recommendations; three are especially useful here:

First, replace blanket prohibitions with an instruction that lets the model read the code around it:

In the new system prompt we say: Write code that reads like the surrounding code: match its comment density, naming, and idiom.

That trades a brittle universal rule for a concrete standard the model can inspect.

Second, move specialized guidance out of the always-on context and load it only when the task calls for it:

Since then, Claude Code has gotten very competent at using progressive disclosure- loading the right context at the right times. For example, we moved verification and code review into their own skills that Claude Code could selectively call.

For design and product agents, that leaves room for richer task-specific standards without crowding every session with irrelevant rules.

Finally, Shihipar on what belongs in CLAUDE.md:

Keep your CLAUDE.md lightweight and briefly describe what your repo is for, but spend most of the tokens on gotchas inside of the codebase. For example, you may organize your code to keep types in one monolithic file and nowhere else. Avoid stating ‘the obvious’ things Claude should know by looking at your file system or your repo.

Use progressive disclosure heavily, for example if you have several unique instructions on how to verify your work, create a verification skill and reference it from your CLAUDE.md.

Product designer and UX Planet editor-in-chief Nick Babich describes software development shifting from production to direction. The design version is familiar by now: as interface production gets cheaper, the work beyond the artifact is defining the right problem, directing agents, evaluating output, and owning the result. Babich traces the same shift in software:

Software development as manual code production is dying.

Software development as translating detailed tickets into predictable implementation is dying.

Software development as a protected technical craft that non-programmers cannot access is dying.

But software engineering is evolving into something broader:

Problem definition + architecture + agent orchestration + verification + product judgment + accountability

This changes the role of the developer.

The most valuable skill is no longer the ability to write code.

It is the ability to define the right problem, provide agents with the right context, evaluate their decisions, and verify that the final result actually works.

Software developers are becoming software creators.

I agree. Replace code with interface production and developer with designer, and Babich could be describing our profession. But there’s a problem both fields share: judgment is built by doing the low-stakes work AI now handles.

Junior developers were historically hired to complete simple implementation tasks such as:

• Build a basic interface;
• Write repetitive tests;
• Fix small bugs;

and these tasks allow them to learn from experienced colleagues and grow professionally.

But in 2026, these tasks are precisely the tasks coding agents handle best.

The loop engineering theme explained how to replace turn-by-turn prompting with a system that discovers work, assigns it, checks the result, and remembers what comes next. AI engineering writer and former Google engineering leader Addy Osmani scales that idea into a software factory and adds the governing constraint: the work can move only as fast as humans can review it.

A software factory is many harnessed loops running at once, fed by a queue of work and drained through a review gate into production, with humans owning the whole thing from above. It is not a bigger agent; it is an org chart made of loops.

Osmani follows that definition through the factory’s wiring diagram:

By and large, every box in this diagram is almost zero cost: generation, tests, scanning. They all run at scale for negligible cost. There is only one expensive box that proves stubbornly resistant to scaling, and that’s the review gate. That shiny amber box is “judgment”, and where the crux of the argument about whether we can make development faster and more frequent resides.

For Osmani, the review gate sets the pace. More generation only helps when verification scales with it; otherwise the factory manufactures a queue of code no one has the attention to understand. Imagine a conveyor belt of products being assembled together, only to pile up at the end, waiting for a poor human quality checker.

Osmani turns that constraint into architecture:

You might be thinking that all sounds unglamorous. You’re right. The safety net is made up of perfectly ordinary architectural practices we’ve always known about and mostly ignored: good types and method signatures so that mistakes are caught by the compiler instead of in production; test seams where we can pin behavior and make change observable; laying out the code so the next reader, human or model, knows where to find the thing they care about; keeping call stacks short and legible; keeping component boundaries well defined so a change doesn’t have a huge blast radius; and dependency injection so we can swap out one piece for another. None of it is new. We’ve always said we care about good architecture. But now that we’re using automated coding agents, that architecture is finally doing a second job as a cheap and hard-to-fake safety net against the mistakes the agent will make.

Put plainly, structure the work so mistakes are easy to spot and changing one thing doesn’t break everything else.

For designers, that means clear rules for each component, documented states, testable prototypes, and explicit review criteria. Build loops that can keep moving without you, but let them move only as far as those checks can prove the work is sound.

Kate Conger, writing in The New York Times, shows how Google’s AI Mode can include links to the open web while making them mostly irrelevant. The product gathers information from other sites, assembles an answer, and keeps the user inside Google. That goes beyond the earlier drop in publisher referrals linked to AI Overviews: Google is reconstructing the trip across the web inside its own interface.

Conger starts with Google co-founder Larry Page’s 2004 promise and the bargain that made Google powerful:

“We believe a well-functioning society should have abundant, free and unbiased access to high-quality information,” Mr. Page said.

Google fulfilled that responsibility by acting as a gateway to the internet. It answered people’s search queries with lists of hyperlinks, pushing users out to what is known as the “open web” — the millions of websites run by merchants, publishers, universities and others — for more information. In the ensuing decades, Google became one of the planet’s richest and most powerful companies by directing people to the vastness of the open web.

Now in the age of artificial intelligence, Google appears to be shrinking back from the open web — and may be imperiling it.

That enclosure is a product behavior, not a login requirement. Conger gives a simple example:

While someone might once have Googled a soccer player’s World Cup performance and clicked to a sports site or a social media thread for commentary, a bot now crawls the web for that information and returns the player’s statistics to the user, who stays on Google.

Google says it still sends billions of clicks and highlights links to the web. But those links now compete with an answer designed to remove the need for them. Conger ends with the contradiction:

In May, Vox Media, which owns The Verge and other publications, began selling some of its sites. Jim Bankoff, Vox Media’s chief executive, said the “decimation of search traffic” had influenced his decision to explore other ways of reaching readers, like a podcast network, and ultimately to sell about half of the company to James Murdoch. In June, The Verge and several other Vox Media websites were sold to Penske Media.

Google has often said it was “sending out more traffic and the web is bigger than ever,” Mr. Patel said. “And then right next to that is a bunch of publishers whose businesses are getting destroyed.”

I’ve started using AI this way in a lot of my daily work. In my day job, after diving into a problem, I give Claude the right context and iterate with it to get to a problem brief. In my freelance work, I set the parameters—the concept, style, or general functionality—then iterate with the AI.

AI product manager and builder Karo Zieminski, writing in Product with Attitude, calls this “AI-assisted craft”:

AI-assisted craft means the human sets the intention and the standard for the work, then directs how it gets made. AI gets a defined supporting role. Supporting, as in: it does not get to make the decisions. The practice applies across knowledge work and digital creation, from writing and research to coding and design.

Zieminski separates assistance from direction:

AI-assisted. The human is the primary maker. The choices, mistakes, revisions, and final form are theirs. AI is one of the tools they use.

AI-directed. The model produces the work; the human directs it. That direction must be consequential enough to shape the result. One prompt followed by a shrug is generation with supervision theatre.

Both require the human to make consequential choices. In my workflows, that happens throughout the iteration, not only when I write the first prompt.

Zieminski’s bounded-task rule makes that concrete:

“Improve this” is not a bounded task.

Give the model a job with sharp edges: do this, not that.

Research for me, BUT bring me facts, not conclusions. Challenge my assumptions, BUT do that through Socratic questions so I have to do the thinking. Explain this code block, BUT test whether I understood it. Suggest design fixes BUT don’t bleach my personality out of it. Whatever it is, leave space for my judgment.

I agree, with one small addition: setting the boundary starts the work. AI can’t answer the question at the other end for me: Would another change improve it, or is it time to stop asking?

Companies may be cutting the very workers AI helps most.

A Stanford Institute for Economic Policy Research policy brief by Neale Mahoney, Erika McEntarfer, and Karsen Wahal finds no economy-wide “AI jobs apocalypse.” But its early evidence creates a possible paradox: employment among less-experienced workers has declined in AI-exposed roles, even as experiments show that AI often gives them the largest productivity boost.

Recent graduates are facing the most challenging job market in years, with unemployment rates for new grads reaching 5.6 percent in early 2026, up 1.6 percentage points from three years earlier. This rise has fueled concerns that AI is replacing many of the jobs recent graduates once sought. Junior roles often involve routine research, analysis, and writing tasks that can now largely be done with AI. Consistent with this intuition is empirical evidence that AI may be dampening demand for new hires.

In a widely discussed paper, Brynjolfsson, Chandar, and Chen report a notable decline in employment among early-career workers in AI-exposed occupations, notably software developers and customer service representatives, since ChatGPT’s launch in 2022. As shown in Figure 2, by contrast, employment among older workers in those same occupations remained relatively stable or continued to grow. The authors liken these young workers to “canaries in the coal mine,” the first to experience labor market disruption from AI. Other researchers have since identified similar negative effects on the hiring of young AI-exposed workers in the U.S. and the U.K., beginning in 2022.

The authors are careful not to pin the entire decline on AI. The Federal Reserve’s rate hikes, the end of pandemic staffing surges, and weaker on-the-job learning in remote roles all muddy the timeline. But the hiring decline itself is real, and younger workers in AI-exposed jobs are taking the hit.

Design’s vanishing bottom rung looks even more precarious against this evidence. The brief does not study designers specifically or establish that AI caused the decline. But if employers are closing junior roles in anticipation of AI, that is a business decision, not a technical inevitability. And the productivity findings make that logic even stranger:

In experimental settings, generative AI tools — such as chatbots and coding tools — have often been found to disproportionately improve the performance of less experienced and poorer performing workers. In one such study, researchers analyzed the impact of a generative AI assistant on customer support agents in a large call center. The assistant increased overall productivity by 15 percent, with gains highly concentrated among novice and less-skilled workers, who saw a 30 percent improvement in the number of issues resolved per hour. There was no performance improvement among highly skilled customer service agents, whose response quality fell slightly.

Other studies also show that AI tools generally speed up task completion, though the effects vary by task, context, and skill level. Figure 3 summarizes research findings on the impact of AI tools on speed across a variety of tasks. In software development, the use of GitHub Copilot — an AI tool that suggests code and functions — allowed tasks to be completed 56 percent faster, with gains concentrated among less-experienced programmers. More modest impacts on software development were found in a separate paper, with effects ranging from 10 percent to 30 percent, depending on the firm where it was deployed.

If companies stop hiring the people who benefit most from these tools, who exactly do they expect to become tomorrow’s senior talent?