Latest / The Feed & The Thread / The Feed & The Thread - September 21, 2026

Transcript

The Feed & The Thread - September 21, 2026 · The Feed & The Thread

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  You can't write an outcome-based OKR when your user is an AI
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  agent. That sounds like a trap we're walking into right now.
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  We're measuring API calls, not human value, and the gap is
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  widening.
• 0:14
  Welcome to The Feed and The Thread, brought to you by
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  Chicago Camps. The Feed & The Thread is available online at
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  feed and thread dot com to submit your feeds, or download
• 0:22
  our app for all the feeds and threads delivered right to your
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  pocket.
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  The question of who holds responsibility when an AI makes
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  a mistake just got a lot harder to answer. We're building
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  systems that act, but we're losing the language to critique
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  their actions. Jeff Gothelf at his own blog makes the case in
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  "How to write an outcome-based OKR when your user is an AI
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  agent" that measuring API calls is a trap because agents have no
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  stake in the outcome. He argues we must look one level up to the
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  human beneficiary, like a traveler accepting a booking,
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  rather than the agent initiating a request. This shift forces us
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  to define value by human impact, not machine activity. Pavel
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  Samsonov at Product Picnic warns in "Assigning agency to AI means
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  surrendering your own" that anthropomorphizing models like
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  Claude causes professionals to surrender their own agency. When
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  we treat code as a colleague, we stop verifying its output,
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  leading to dangerous chains of unaccountable information. This
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  linguistic drift turns a tool into a oracle, and we lose the
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  critical thinking that keeps us safe. Hiroshi Sato at UX
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  Design.cc explores the need for new paradigms in "AI nutrition
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  facts", arguing that AI is a participant in the narrative,
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  not just a generator. He calls for clear feedback loops that
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  let writers see how the system interprets their intent before
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  they commit to the final output. Without this transparency, we're
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  designing for a collaboration that doesn't actually exist.
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  Other articles today from Smashing Magazine on design
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  tokens and UIE on facilitation traps.
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  A developer is asking for help making their AI-generated
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  interface look less depressing. Over on r/UI_Design, a new
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  developer is asking for tools that fix the bland, lifeless
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  output from their current AI design system. They want a UI
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  that feels human, not like a default template. This
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  highlights a growing gap where the speed of generation is
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  outpacing the quality of intent. We're building fast, but we're
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  losing the nuance. In r/UI_Design, someone shared the
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  evolution of their expense tracker, moving from a simple
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  list to a block-based category layout in version two. The
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  improvement is clear because grouping categories into
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  distinct blocks enhances visual scanning. It shows that even
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  small structural changes can make a complex data set feel
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  manageable and easy to use. Over on r/UI_Design, a designer
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  is asking which Chrome extensions actually save time
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  for inspecting typography and spacing. This is a practical
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  question about precision. In a field where details matter,
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  having the right tools to measure and verify your work is
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  essential for maintaining quality and consistency. In
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  r/ProductDesign, a junior designer is expressing
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  frustration with the expectation to complete unpaid personal
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  projects to prove their worth. They question the sustainability
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  of this model, noting that design work is draining. This
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  touches on the real human cost of entry into the profession and
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  the need for more equitable hiring practices. Over on
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  r/web_design, a small business owner is looking for a simple,
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  secure CMS for a three-page website. They're hesitant about
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  WordPress due to security risks but find custom development too
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  expensive. This is a common dilemma where budget constraints
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  and security needs collide, and finding a balanced solution is
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  key for small teams. Today, the focus is on the human effort
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  behind the screens, from the frustration of AI defaults to
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  the real-world costs of entry into the design field.
• 3:53
  The Feed and The Thread is brought to you by Chicago Camps.
• 3:56
  For a weekly and monthly exploration of the patterns and
• 3:59
  signals across UX, you can also check out Patterns and Signals
• 4:02
  at the website patterns and signals dot com. Chicago Camps
• 4:05
  will have more events in 2027. You can submit to be a speaker
• 4:08
  at a future event at Chicago Camps dot org.
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  The thread from r/UI_Design about AI outputs feeling "bland"
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  and "lifeless" sits uncomfortably next to Pavel
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  Samsonov's warning that we're surrendering our own agency.
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  We're treating the model as a colleague, which means we're
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  stopping the verification process, and the result is that
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  generic, template-like output we keep seeing. But I think we're
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  looking at the wrong problem. It's not that the AI is bad,
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  it's that we're asking it to do the thinking for us. When we
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  stop verifying, we lose the nuance. The "blandness" is a
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  symptom of us outsourcing the judgment call. If you don't
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  interrogate the intent, you get the average. That's a fair
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  point, but it ignores the structural issue Samsonov
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  raises. If the language shifts to "colleague," the
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  psychological barrier to checking the work goes up. You
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  don't fact-check a friend's opinion the same way you fact-
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  check a machine. We're losing the critical distance required
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  to spot when the system is just guessing. I agree that the
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  distance matters, but the solution is to change what we
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  ask it to do. Instead of asking for the final answer, we need to
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  ask for the reasoning. We need to build feedback loops where
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  the human sees the interpretation before committing
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  to the output. That's the "nutrition facts" Sato talks
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  about. Exactly. And that connects to the "outcome-based
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  OKR" piece. If we measure success by human impact, we
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  force that verification step. We stop valuing the speed of
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  generation and start valuing the quality of the decision. The
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  agent has no stake in the outcome, so the human has to
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  carry the weight. Right. And that's where the opportunity is.
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  The skill isn't prompting. It's editing. It's knowing which
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  parts of the AI's suggestion to keep and which to throw away
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  because the intent was off. That judgment is the new standard.
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  So the shift is from asking "how do I make it generate faster?"
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  to "how do I make my judgment visible?" And that judgment is
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  the only thing that can't be automated. It's the human stake
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  in the outcome.
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  That's The Feed and The Thread for today. We'll catch you next
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  time!