Latest / The Feed & The Thread / The Feed & The Thread - September 2, 2026

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The Feed & The Thread - September 2, 2026 · The Feed & The Thread

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  You can't mandate an AI transformation from the top
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  down. So what happens when leadership tries anyway. It
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  breeds performative compliance rather than genuine innovation,
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  and we're breaking down why fear fails here.
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  Welcome to The Feed and The Thread, brought to you by
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  Chicago Camps. Leadership By Design is on Thursday and
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  Friday, September 17 and 18 and tickets are available now! And
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  while you're at it, get caught up on UX fundamentals with five
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  minute UX at five em UX dot com. The Feed & The Thread is
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  available online at feed and thread dot com to submit your
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  feeds, or download our app for all the feeds and threads
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  delivered right to your pocket.
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  We often assume AI is just a faster tool, but the real
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  friction is human. We're building systems that outpace
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  our ability to interpret them, or our willingness to change how
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  we work. Fernanda Villalobos and Jeff Sauro at MeasuringU
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  make the case in "Nine Design Fixes for Your UX Research
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  Reports" that clarity beats volume. They argue that while AI
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  can draft reports, it can't fix the fundamental problem of
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  limited attention spans. The fix isn't more data; it's a rigid
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  structure with an executive summary and high-resolution
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  imagery. If stakeholders can't digest the findings in seconds,
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  the research fails. Patrick Neeman at UX Design.cc argues in
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  "What Minority Report got right about AI" that we've been
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  distracted by the wrong sci-fi tropes. The gesture controls in
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  Minority Report were a decoy, masking a much more accurate
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  prediction of generative AI. The film's precogs function exactly
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  like large language models, synthesizing plausible
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  narratives from noise. We've obsessed over visual spectacle
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  while ignoring the usability reality that those interfaces
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  were physically exhausting to use. Julie Zhuo writes in "You
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  Cannot Mandate an AI Transformation" that you can't
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  mandate an AI transformation from the top down. Fear-based
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  directives lead to performative compliance, not genuine
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  innovation. She proposes a four- part framework starting with
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  automating hated tasks to build personal belief. The bottleneck
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  isn't the technology; it's our human capacity to adapt from
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  skepticism to curiosity. Other reads today from Scott Berkun on
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  feeling ignored, UX Planet on Claude Code, and Jeffrey Zeldman
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  on suffering fools.
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  Designers are wrestling with a specific kind of friction today.
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  It isn't about the speed of new tools. It's about the human gap
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  between building systems and understanding them. Over on
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  r/UXResearch, someone is asking whether synthetic research has a
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  place in our workflow. The post argues we shouldn't dismiss it
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  entirely, but we must not treat it as a replacement for real
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  human insight. The suggestion is to use it for narrow tasks like
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  quick pre-testing. This matters because it highlights a tension
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  between efficiency and depth. We need to know where the data ends
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  and the human truth begins. In r/UXResearch, another poster
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  shares a frustrating experience with a recruiter who ghosted
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  them after a verbal offer. The recruiter then falsely told the
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  hiring manager the candidate was uninterested. This reveals a
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  breakdown in professional ethics that goes beyond a bad hiring
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  process. It shows how communication failures can
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  derail careers. We have to protect our own narratives when
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  the system fails to communicate clearly. A psychology student
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  with an IT background is asking if pivoting to UX research is
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  still worth it. They're concerned about market
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  saturation and the practical use of psychology in daily work.
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  This question reflects a broader anxiety about entry-level
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  viability. It forces us to look at what skills actually transfer
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  and which ones don't. The field needs honest answers about where
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  academic theory meets industrial reality. Over on r/ScienceUX, a
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  professional working on medical hardware is looking for courses
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  on accessibility and human factors. They note that most
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  training focuses on web products, leaving a gap for
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  embedded systems. This highlights a blind spot in our
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  education. We often assume accessibility guidelines are
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  universal, but physical interfaces require different
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  rigor. Finding resources for these specialized contexts is a
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  challenge many face. Finally, in r/ProductDesign, someone is
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  asking about the quality of LCI Barcelona for a master's degree.
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  They want to know if the school offers intense training and
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  direct company connections. This is a practical question about
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  career investment. It reminds us that design education is often
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  about access as much as it's about skill. We need to weigh
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  the cost against the actual network value. Today's
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  discussions show that the hardest part of design isn't the
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  tool. It's navigating the human systems around it.
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  Chicago Camps is hosting Leadership By Design on Thursday
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  & Friday, September 17 & 18. It's an online event, so you can
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  join from anywhere in the world! Tickets are free, thanks to the
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  generosity of the community! If it's within your budget, you can
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  purchase a general admission ticket for only twenty six
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  dollars - with limited early bird tickets at only fifteen
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  dollars. Get tickets now at Chicago Camps dot org.
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  Julie Zhuo argues that fear- based mandates create
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  performative compliance rather than genuine innovation. She
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  suggests starting with automating hated tasks to build
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  personal belief. The bottleneck is human adaptation, not the
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  technology itself. That framework makes sense in theory.
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  But looking at the community anxiety about entry-level
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  viability, I worry we're skipping the adaptation phase
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  entirely. Designers are asking if synthetic research replaces
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  real insight. If we automate the grunt work too fast, we lose the
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  curiosity that comes from doing it manually. I see the tension,
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  but I think we're conflating efficiency with understanding.
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  The MeasuringU article points out that clarity beats volume.
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  If AI handles the data synthesis, researchers have more
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  time to structure findings for stakeholder attention spans. The
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  tool doesn't remove the need for judgment. It just removes the
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  drudgery of compilation. Only if the organization values that
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  judgment as a distinct output. Right now, many teams treat
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  research as a data dump. If you automate the dump, you might
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  just get a faster, emptier result. The risk isn't that we
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  lose the work. It's that we lose the signal in the noise because
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  we stopped listening to the raw human input. So the issue isn't
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  the automation. It's the reporting structure. If we keep
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  using rigid executive summaries and high-resolution imagery as
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  the standard, the AI becomes a helper. If we rely on volume to
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  prove value, the AI becomes a threat. The shift is in how we
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  define a successful deliverable. Exactly. We need to stop
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  measuring research by how much data it produces. We need to
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  measure it by how quickly a stakeholder can act on it. The
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  tool is just the vehicle. The destination is decision speed.
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  The metric shouldn't be the report length. It should be the
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  time between finding and action. Agreed. When the goal is
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  clarity, the noise disappears regardless of the tool.
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  That's The Feed and The Thread for today. We'll catch you next
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  time!