I’ve seen this at every company past a certain size: you spot a disjointed UX problem across the product, you know what needs to happen, and then you spend three months in alignment meetings trying to get six teams to agree on a button style.

A recent piece from Laura Klein at Nielsen Norman Group examines why most product teams aren’t actually empowered, despite what the org chart claims. Klein on fragmentation:

When you have dozens of empowered teams, each optimizing its own metrics and building its own features, you get a product that feels like it was designed by dozens of different companies. One team’s area uses a modal dialog for confirmations. Another team uses an inline message. A third team navigates to a new page. The buttons say Submit in one place, Save in another, and Continue in a third. The tone of the microcopy varies wildly from formal to casual.

Users don’t see teams. They don’t see component boundaries. They just see a confusing, inconsistent product that seems to have been designed by people who never talked to each other, because, in a sense, it was.

Each team was empowered to make the best decisions for their area, and it did! But nobody was empowered to maintain coherence across the whole experience.

That last line is the whole problem. “Coherence,” as Klein calls it, is a design leadership responsibility, and it gets harder as AI lets individual teams ship faster without coordinating with each other. If every squad can generate production UI in hours instead of weeks, the fragmentation described here accelerates. Design systems become the only thing standing between your product and a Frankenstein experience.

The article is also sharp on what happens to PMs inside this dysfunction:

Picture a PM who spends 70% of her time in meetings coordinating with other teams, getting buy-in for a small change, negotiating priorities, trying to align roadmaps, escalating conflicts, chasing down dependencies, and attending working groups created to solve coordination problems. She spends a tiny fraction of her time with users. The rest is spent writing documents that explain her team’s work to other teams, updating roadmaps, reporting status, and attending planning meetings. She was hired to be a strategic product thinker, but she’s become a project manager, focused entirely on logistics and coordination.

I’ve watched this happen to PMs I’ve worked with. The coordination tax eats the strategic work. Marty Cagan calls this “product management theater”—a surplus of PMs who function as overpaid project managers. If AI compresses the engineering work but the coordination overhead stays the same, that ratio gets even more lopsided.

The fix is smaller teams with real ownership and strong design systems that enforce coherence without requiring 14 alignment meetings. But that requires organizational courage most companies don’t have.

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

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

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

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

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

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

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

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

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

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

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

There’s a version of product thinking that lives in frameworks and planning docs. And then there’s the version that shows up when someone looks at a screen and immediately knows something is off. That second version—call it product sense, call it taste or judgement—comes from doing the work, not reading about it.

Peter Yang, writing in his Behind the Craft newsletter, shares 25 product beliefs from a decade at Roblox, Reddit, Amazon, and Meta. The whole list is worth reading, but a few items stood out.

On actually using your own product:

I estimate that less than 10% of PMs actually dogfood their product on a weekly basis. Use your product like a first-time user and write a friction log of how annoying the experience is. Nobody is too senior to test their own shit.

Ten percent. If that number is even close to accurate, it’s damning. You can’t develop good product judgment if you’re not paying attention to the thing you ship. And this applies to designers just as much as PMs.

Yang again, on where that judgment actually shows up:

Default states, edge cases, and good copy — these details are what separates a great product from slop. It doesn’t matter how senior you are, you have to give a damn about the tiniest details to ship something that you can be proud of.

Knowing that default states matter, knowing which edge cases to care about, knowing when copy is doing too much or too little—you can’t learn that from a framework. That’s pattern recognition from years of seeing what good looks like and what falls apart.

And on what qualifies someone to do this work:

Nobody cares about your FAANG pedigree or AI product certificate. Hire high agency people who have built great side projects or demonstrated proof of work. The only credential that matters is what you’ve shipped and your ideas to improve the product.

Reps and shipped work, not reading and credentials. The people who’ve done the reps are the ones who can see the details everyone else misses.

Every designer I’ve managed who made the leap from good to great had one thing in common: they understood why things work, not just how to make them look right. They had product sense. And most of them didn’t learn it from a PM book.

Christina Wodtke, writing for Eleganthack, frames product sense as “compressed experience”:

Product sense works the same way. When a seasoned PM looks at a feature and immediately knows it’s wrong, they’re not being mystical. Their brain is processing hundreds of micro-signals: user flow friction, business model misalignment, technical complexity, competitive dynamics. Years of experience get compressed into a split-second gut reaction.

Swap “PM” for “designer” and this is exactly how design leadership works. The best design critiques I’ve been in aren’t about color choices or spacing—they’re about someone sensing that a flow is wrong before they can articulate why. That’s compressed experience doing its job.

Wodtke’s piece is aimed at product managers, but I think designers need it more. PMs at least have the business context baked into their role. Designers can spend years getting really good at craft without ever building the pattern recognition that tells them what to design, not just how.

This is the part that should be required reading for every designer:

Most people use apps passively — they open Spotify, play music, done. Product people need to use apps actively; not as a user but like a UX designer. They notice the three-tap onboarding flow. They see how the paywall appears after exactly the right amount of value demonstration. They understand why the search bar is positioned there, not there.

Wodtke literally says “like a UX designer.” That’s the standard she’s holding PMs to. So what’s our excuse?

She also nails why reading about product thinking isn’t enough:

Most people try to build product sense by reading about it. That’s like trying to learn tennis by studying physics. You need reps.

The designers on my team who do this—who actively pull apart flows, question trade-offs, study what real products actually ship—are the ones I can’t live without. They don’t need a spec to have an opinion. They already have the reps and consistently impress their PM counterparts.

Wodtke built a nine-week curriculum for her Stanford students that walks through onboarding, checkout, search, paywalls, error states, personalization, UGC, accessibility, and growth mechanics. Each week compares how three different products solve the same problem differently. It’s the kind of thing I wish I could assign to every junior designer on my team.

If you’re a designer and you’re only studying visual references on Dribbble, you’re doing half the work. Go do these exercises.

For as long as I’ve been in startups, execution speed has been the thing teams optimized for. The assumption was always that if you could just build faster, you’d win. That’s your moat. AI has mostly delivered on that promise—teams can now ship in weeks—see Claude Cowork—what used to take months. And the result is that a lot of teams are building the wrong things faster than ever.

Gale Robins, writing for UX Collective, opens with a scene I’ve lived through from both sides of the table:

I watched a talented software team present three major features they’d shipped on time, hitting all velocity metrics. When I asked, “What problem do these features solve?” silence followed. They could describe what they’d built and how they’d built it. But they couldn’t articulate why any of it mattered to customers.

Robins argues that judgment has replaced execution as the real constraint on product teams. And AI is making this worse, not better:

What once took six months of misguided effort now takes six weeks, or with AI, six days.

Six days to build the wrong thing. The build cycle compressed but the thinking didn’t. Teams are still skipping the same discovery steps, still assuming they know what users want. They’re just doing it at a pace that makes the waste harder to catch.

Robins again:

AI doesn’t make bad judgment cheaper or less damaging — it just accelerates how quickly those judgment errors compound.

She illustrates this with a cascade example: a SaaS company interviews only enterprise clients despite SMBs making up 70% of revenue. That one bad call—who to talk to—ripples through problem framing, solution design, feature prioritization, and evidence interpretation, costing $315K over ten months. With AI-accelerated development, the same cascade plays out in five months at the same cost. You just fail twice as fast.

The article goes on to map 19 specific judgment points across the product discovery process. The framework itself is worth a read, but the underlying argument is the part I keep coming back to: as execution gets cheaper, the quality of your decisions is the only thing that scales.

The data from Lenny’s Newsletter’s AI productivity survey showed PMs ranking prototyping as their #2 use case for AI, ahead of designers. Here’s what that looks like in practice.

Figma is now teaching PMs to build prototypes instead of writing PRDs. Using Figma Make, product managers can go from idea to interactive prototype without waiting on design. Emma Webster writing in Figma’s blog:

By turning early directions into interactive, high-fidelity prototypes, you can more easily explore multiple concepts and take ideas further. Instead of spending time writing documentation that may not capture the nuances of a product, prototypes enable you to show, rather than tell.

The piece walks through how Figma’s own PMs use Make for exploration, validation, and decision-making. One PM prototyped a feature flow and ran five user interviews—all within two days. Another used it to workshop scrolling behavior options that were “almost impossible to describe” in words.

The closing is direct about what this means for roles:

In this new landscape, the PMs who thrive will be those who embrace real-time iteration, moving fluidly across traditional role boundaries.

“Traditional role boundaries” being design’s territory.

This isn’t a threat if designers are already operating upstream—defining what to build, not just how it looks. But if your value proposition is “I make the mockups,” PMs now have tools to do that themselves.

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

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

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

Meanwhile, founders are using AI to think—for decision support, product ideation, and strategy. They treat it as a thought partner, not a production tool. And product managers are building prototypes themselves:

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

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

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

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

It’s January and by now millions of us have made resolutions and probably broken them already. The second Friday of January is known as “Quitter’s Day.”

OKRs—objectives and key results—are a method for businesses to set and align company goals. The objective is your goal and the KRs are the ways to reach your goals. Venture capitalist John Doerr learned about OKRs while working at Intel, brought it to Google, and later became the framework’s leading evangelist.

Christina Wodtke talks about how to use OKRs for your personal life, and maybe as a way to come up with better New Year’s resolutions. She looked at her past three years of personal OKRs:

Looking at the pattern laid out in front of me, I finally saw what I’d been missing. My problem wasn’t work-life balance. My problem was that I didn’t like the kind of work I was doing.

The key results kept failing because the objective was wrong. It wasn’t about balance. It was about joy.

This is the second thing key results do for you: when they consistently fail, they’re telling you something. Not that you lack discipline—that you might be chasing the wrong goal entirely.

And I love Wodtke’s line here: “New Year’s resolutions fail because they’re wishes, not plans.“ She continues:

They fail because “eat better” and “be healthier” and “find balance” are too vague to act on and too fuzzy to measure.

Key results fix this. Not because measurement is magic, but because the act of measuring forces clarity. It makes you confront what you actually want. And sometimes, when the data piles up, it reveals that what you wanted wasn’t the thing you needed at all.

Building on our earlier link about measuring the impact of features, how can we keep track of the overall health of the product? That’s where a North Star Metric comes in.

Julia Sholtz writes and introduction to North Star Metrics in the analytics provider Amplitude’s blog:

Your North Star Metric should be the key measure of success for your company’s product team. It defines the relationship between the customer problems your product team is trying to solve and the revenue you aim to generate by doing so.

How is it done? The first step is to figure out the “game” your business is playing: how your business engages with customers:

1. The Attention Game: How much time are your customers willing to spend in your product?
2. The Transaction Game: How many transactions does your user make on your platform?
3. The Productivity Game: How efficiently and effectively can someone get their work done in your product?

They have a whole resource section on this topic that’s worth exploring.

How do we know what we designed is working as intended? We measure. Vitaly Friedman shares something called the TARS framework to measure the impact of features.

We need UX metrics to understand and improve user experience. What I love most about TARS is that it’s a neat way to connect customers’ usage and customers’ experience with relevant product metrics.

Here’s TARS in a nutshell:

• Target Audience (%): Measures the percentage of all product users who have the specific problem that a feature aims to solve.
• Adoption (%): Tracks the percentage of the target audience that successfully and meaningfully engages with the feature.
• Retention (%): Assesses how many users who adopted the feature continue to use it repeatedly over time.
• Satisfaction Score (CES): Gauges the level of satisfaction, specifically how easy it was for retained users to solve their problem after using the feature.

Friedman has more details in the article, including how to use TARS to measure how well a feature is performing for your intended target audience.

I really appreciate the perspective of Lai-Jing Chu here as a Silicon Valley veteran. The struggle to prove the value of design is real.

I don’t know another function or role in the tech industry where it seems like we have to do our jobs at the same time as — and I will avoid saying “demonstrating value” here because it’s more than that — we carry out some sort of divine duty to make the product (let alone the world) a better place through our creativity.

Instead of more numbers like ROI calculations, Chu argues for counterintuitive approaches for advocacy, “not more left-brain exercises.”

Chu introduces us to W. Edwards Deming, an influential management consultant who wrote:

The most important figures needed for management of any organization are unknown and unknowable, but successful management must nevertheless take account of them.

One strategy she offers is to ask leadership a common-sense question: How would you grade the design?

Because when was the last time anyone did the most basic thing — to stop for a moment, hold the product in their hands, and take a good hard look at it? These questions throw the ball back in their court. It makes them wonder what they can do to help. Because chances are, most leaders want their product to have a good user or customer experience and understand that it makes a difference to their business success. You don’t just want buy-in — you want them to have true ownership.

I admire this approach, because chances are, leaders are already hearing about UX issues from customers. But to put this into practice in, let’s say, at any startup post-Series A will be an issue. There’s a lot of coordination and alignment that needs to happen because exec-level attention is much harder to come by.

Here is a good reminder from B. Prendergast to “stop asking users what they want—and start watching what they do.”

Asking people what they want is one of the most natural instincts in product work. Surveys, interviews, and feature wish lists feel accessible, social, and collaborative. They open channels to understand and empathise with the user base. They help teams feel closer to the people they serve. For teams under pressure, a stack of opinions can feel like solid data.

But this breaks when we compare what users say to what they actually do (say-do gap).

We all want to present ourselves a certain way. We want to seem more competent than confused (social desirability bias). Our memories can be fuzzy, especially about routine tasks (recall bias). Standards for what feels “easy” or “intuitive” can vary wildly between people (reference bias).

And of course, as soon as we start to ask users to imagine what they’d want, they’ll solve based on their personal experiences—which might be the right solution for them, but might not be for other users in the same situation.

Prendergast goes on to suggest “watch what people do, measure what matters, and use what they say to add context.” This approach involves watching user interactions, analyzing real behaviors through analytics, and treating feature requests as signals of underlying problems to uncover genuine needs. Prioritizing decisions based on observed patterns and desired outcomes leads to more effective solutions than relying on user opinions alone.

Product manager Adrian Raudaschl offered some reflections on 2025 from his point of view. It’s a mixture of life advice, product recommendations, and thoughts about the future of tech work.

The first quote I’ll pull out is this one, about creativity and AI:

Ultimately, if we fail to maintain active engagement with the creative process and merely delegate tasks to AI without reflection, there is a risk that delegation becomes abdication of responsibility and authorship.

“Active engagement” with the tasks that we delegate to AI. This reminds me of the humble machines argument by Dr. Maya Ackerman.

On vibe coding:

The most important thing, I think, that most people in knowledge work should be doing is learning to vibe code. Vibe code anything: a diary, a picture book for your mum, a fan page for your local farm. Anything. It’s not about learning to code, but rather appreciating how much more we could do with machines than before. This is what I mean about the generalist product manager: being able to prototype, test, and build without being held back by technical constraints.

I concur 100%. Even if you don’t think you’re a developer, even if you don’t quite understand code, vibe coding something will be illuminating. I think it’s different than asking ChatGPT for a bolognese sauce recipe or how to change a tire. Building something that will instantly run on your computer and seeing the adjustments made in real-time from your plain English prompts is very cool and gives you a glimpse into how LLMs problem-solve.

AI threatens to let product teams ship faster. Faster PRDs, faster designs, and faster code. But going too fast can often lead to incurring design and tech debt, or even worse, shipping the wrong thing.

Anton Sten sagely warns:

The biggest pattern I have seen across startups is that skipping clarity never saves time. It costs time. The fastest teams are not the ones shipping the most. They are the ones who understand why they are shipping. That is the difference between moving for the sake of movement and moving with purpose. It is the difference between speed and true velocity.

How do you avoid this? Sten:

The reset is simple and almost always effective. Before building anything, pause long enough to ask, “What problem am I solving, and for whom?” It sounds basic, but this question forces alignment. It replaces assumptions with clarity and shifts attention back to the user instead of internal preferences. When teams do this consistently, the entire atmosphere changes. Decisions become easier. Roadmaps make more sense. People contribute more of themselves. You can feel momentum return.

Imagine working for seven years designing the prototyping features at Figma and then seeing GPT-4 and realizing what AI can soon do in the future. That’s the story of Figma designer–turned–product manager Nikolas Klein. He shares his journey via a lovely illustrated comic—Webtoon style.

Klein emphasizes:

The truth is: There will always be new problems to solve. New ideas to take further. Even with AI, hard problems are still hard. An answer may come faster, but it’s not always right.

We’ve been hearing a lot about AI agents and now enough time has passed that we’re starting to see some learnings in industry. Writing in Harvard Business Review, Linda Mantia, Surojit Chatterjee and Vivian S. Lee showcase three case studies of enterprises that have deployed AI agents.

They write about Hitachi Digital and how they deployed an AI agent as the first responder to the 90,000 questions employees send to their HR team annually.

Every year, employees put over 90,000 questions about everything from travel policies and remote work to training and IT support to the company’s HR team of 120 human responders. Answering these queries can be difficult, in part because of Hitachi’s complex infrastructure of over 20 systems of record, including multiple disparate HR systems, various payroll providers, and different IT environments.

Their system, called “Skye,” is actually a system of agents, coordinating with one another and firing off queries depending on the intent and task.

For example, the intent classifier agent sends a simple policy question like “What are allowed expenses for traveling overseas?” or “Does this holiday count in paid time off?” to a file search and respond agent, which provides immediate answers by examining the right knowledge base given the employee’s position and organization. A document generation agent can create employee verification letters (which verify individuals’ employment status) in seconds, with an option for human approval. When an employee files a request for vacation, the leave management agent uses the appropriate HR management system based on its understanding of the user’s identity, completes the necessary forms, waits for the approval of the employee’s manager, and reports back to the employee.

The authors see three essential imperatives when designing and deploying AI agents into companies.

1. Design around outcomes and appoint accountable mission owners. Companies need to stop organizing around internal functions and start building teams around actual customer outcomes—which means putting someone in charge of the whole journey, not just pieces of it.
2. Unlock data silos and clarify the business logic. Your data doesn’t need to be perfect or centralized, but you do need to map out how work actually gets done so AI agents know where to find things and what decisions to make.
3. Develop the leaders and guardrails that intelligent systems require. You can’t just drop AI agents into your org and hope for the best—leaders need to understand how these systems work, build trust with their teams, and put real governance in place to keep things on track.

Matt Ström-Awn makes the argument that companies can achieve sustainable excellence by empowering everyone at each level to take ownership of quality, rather than relying solely on top-down mandates or standardized procedures.

But more and more I’ve come to believe that quality isn’t a slogan, a program, or a scorecard. It’s a promise kept at the edge by the people doing the work. And, ideally, quality is fundamental to the product itself, where users can judge it without our permission. That’s the shift we need: away from heroics at the center, toward systems that make quality inevitable.

The stakes are high. Centralized quality — slogans, KPIs, executive decrees — can produce positive results, but it’s brittle. Decentralized quality — continuous feedback, distributed ownership, emergent standards — builds resilience. In this essay, I’d like to make the case that the future belongs to those who can decentralize their mindset and approach to quality.

Ström-Awn offers multiple case studies, contrasting centralized systems with decentralized ones, using Ford, Amazon, Apple, Toyota, Netflix, 3M, Morning Star, W.L. Gore, Valve, Barnes & Noble, and Microsoft under Satya Nadella as examples.

These stories share a common thread: organizations that trusted their frontline workers to identify and solve quality problems. But decentralized quality has its own vulnerabilities. Valve’s radical structure has been criticized for creating informal power hierarchies and making it difficult to coordinate large projects. Some ex-employees describe a “high school clique” atmosphere where popular workers accumulate influence while others struggle. Without traditional management oversight, initiatives can moulder, or veer in directions that don’t serve broader company goals.

Still, these examples show a different path for achieving quality, where excellence is defined in the course of building a product. Unlike centralized approaches relying on visionary (but fallible) leaders, decentralized systems are resilient to individual failures, adaptable to change, and empowering to builders. The andon cord, the rolling desk, and the local bookstore manager each represent a small bet on human judgment over institutional control. Those bets look like they’re paying off.

OK, so there’s workslop, but there’s also general AI slop. With OpenAI’s recent launch of the Sora app, there going to be more and more AI-generated image and video content making the rounds. I do believe that there’s a place for using AI to generate imagery. It can be done well (see Christian Haas’s “AI Jobs”). Or not.

Casey Newton, writing in his Platformer newsletter:

In Sora we find the entire debate over AI-generated media in miniature. On one hand, the content now widely derided as “slop” continually receives brickbats on social media, in blog posts and in YouTube comments. And on the other, some AI-generated material is generating millions of views — presumably not all from people who are hate-watching it.

As the content on the internet is increasingly AI-generated, platforms will need to balance how much of it they let in, lest the overall quality drops.

As Sarah Perez noted at TechCrunch, Pinterest has come under fire from its user base all year for a perceived decline in quality of the service as the percentage of slop there increases. Many people use the service to find real objects they can buy and use; the more that those objects are replaced with AI fantasies, the worse Pinterest becomes for them.

Like most platforms, Pinterest sees little value in banning slop altogether. After all, some people enjoy looking at fantastical AI creations. At the same time, its success depends in some part on creators believing that there is value in populating the site with authentic photos and videos. The more that Pinterest’s various surfaces are dominated by slop, the less motivated traditional creators may be to post there.

When I read this, I thought to myself, “Geez, this is what a designer does.” I think there is a lot of overlap between what we do as product designers and what product managers do. One critical one—in my opinion, and why we’re calling ourselves product designers—is product sense. Product sense is the skill of finding real user needs and creating solutions that have impact.

So I think people can read this with two lenses:

• If you’re a designer who executes the assignments you’re given, jumping into Figma right away, read this to be more well-rounded and understand the why of what you’re making.
• If you’re a designer who spends 80% of your time questioning everything and defining the problem, and only 20% of your time in Figma, read this to see how much overlap you actually have with a PM.

BTW, if you’re in the first bucket, I highly encourage you to gain the skills necessary to migrate to the second bucket.

While designers often stay on top of visual design trends or the latest best practices from NNG, Jules Walter suggests an even wider aperture. Writing in Lenny’s Newsletter:

Another practice for developing creativity is to spend time learning about emerging trends in technology, society, and regulations. Changes in the industry create opportunities for launching new products that can address user needs in new ways. As a PM, you want to understand what’s possible in your domain in order to come up with creative solutions.

The headline rings true to me because that’s what I look for in designers and how I run my team. The software that we build is too complex and too mission-critical for designers to vibe-code—at least given today’s tooling. But each one of the designers on my team can fill in for a PM when they’re on vacation.

Kai Wong, writing in UX Collective:

One thing I’ve learned, talking with 15 design leaders (and one CEO), is that a ‘designer who codes’ may look appealing, but a ‘designer who understands business’ is far more valuable and more challenging to replace.

You already possess the core skill that makes this transition possible: the ability to understand users with systematic observation and thoughtful questioning.

The only difference, now, is learning to apply that same methodology to understand your business.

Strategic thinking doesn’t require fancy degrees (although it may sometimes help).

Ask strategic questions about business goals. Understand how to balance user and business needs. Frame your design decisions in terms of measurable business impact.

Miquad Jaffer, a product leader at OpenAI shares his 4D method on how to build AI products that users want. In summary, it’s…

• Discover: Find and prioritize real user pain points and friction in daily workflows.
• Design: Make AI features invisible and trustworthy, fitting naturally into users’ existing habits.
• Develop: Build AI systematically, with robust evaluation and clear plans for failures or edge cases.
• Deploy: Treat each first use like a product launch, ensuring instant value and building user trust quickly.

This post has been swimming in my head since I read it. Elena Verna, who joined Lovable just over a month ago to lead marketing and growth, writing in her newsletter, observes that everyone at the company is an AI-native employee. “An AI-native employee isn’t someone who ‘uses AI.’ It’s someone who defaults to AI,” she says.

On how they ship product:

Here, when someone wants to build something (anything) - from internal tools, to marketing pages, to writing production code - they turn to AI and… build it. That’s it.

No headcount asks. No project briefs. No handoffs. Just action.

At Lovable, we’re mostly building with… Lovable. Our Shipped site is built on Lovable. I’m wrapping hackathon sponsorship intake form in Lovable as we speak. Internal tools like credit giveaways and influencer management? Also Lovable (soon to be shared in our community projects so ya’ll can remix them too). On top of that, engineering is using AI extensively to ship code fast (we don’t even really have Product Managers, so our engineers act as them).

I’ve been hearing about more and more companies operating this way. Crazy time to be alive.

More on this topic in a future long-form post.

In a dual profile, Ben Blumenrose spotlights Phil Vander Broek—whose startup Dopt was acquired last year by Airtable—and Filip Skrzesinski—who is currently working on Subframe—in the Designer Founders newsletter.

One of the lessons Vander Broek learned was to not interview customers just to validate an idea. Interview them to get the idea first. In other words, discover the pain points:

They ran 60+ interviews in three waves. The first 20 conversations with product and growth leaders surfaced a shared pain point: driving user adoption was painfully hard, and existing tools felt bolted on. The next 20 calls helped shape a potential solution through mockups and prototypes—one engineer was so interested he volunteered for weekly co-design sessions. A final batch of 20 calls confirmed their ideal customer was engineers, not PMs.

As for Skrzesinski, he’s learning that being a startup founder isn’t about building the product—it’s about building a business:

But here’s Filip’s counterintuitive advice: “Don’t start a company because you love designing products. Do it in spite of that.”

“You won’t be designing in the traditional sense—you’ll be designing the company’s DNA,” he explains. “It’s the invisible work: how you organize, how you think, how you make decisions. How it feels to work there, to use what you’re making, to believe in it.”

Brian Balfour, writing for the Reforge blog:

Speed isn’t just about shipping faster, it’s about accelerating your entire learning metabolism. The critical metric isn’t feature velocity but rather your speed through the complete Insight → Act → Learn loop. This distinction separates products that compound advantages from those that compound technical debt.

The point being that now with AI, product teams are shipping faster. And those who aren’t might get lapped (to use an F1 phrase).