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Using An App To Get Off Your Phone, And The Research That Says AI Is Affecting Our Brain
📱 Bond — The Social Media App That Wants To Cure Your Doom-Scrolling — TechCrunch
Bond launched this week as a social media platform explicitly designed to get you off your phone — no infinite feed, no algorithmic scroll, just a spatial view of what your friends are up to and activity recommendations based on your interests
The core bet: remove the vertical feed and you remove the addictive pattern — the app gives you ideas for real-world activities, you go live them, you get off the app
I haven't tested it, but I have a lot of thoughts
First: using an app to get off your phone is paradoxical — your phone is still your phone, and everything else addictive is still on it
Second: removing the feed doesn't remove social comparison — seeing what friends are up to, peeking at their memories, knowing they got a promotion — that's still there, and social comparison is one of the more reliably damaging patterns in existing platforms
Third — and this one I can't let go: end-to-end encryption is described as "a priority for us in the near future after launch" — meaning right now, the team can see your data — storing data securely is not the same as private data
The monetisation path is also unresolved — licensing user data to AI companies and product recommendations with merchant commissions are both on the table
My honest read: the intent seems genuine, but the medium is still a phone, the social comparison patterns are still present, and the privacy foundations aren't there yet
🧠 Concerns Grow That AI Is Damaging Users' Cognitive Abilities — Futurism
MIT researchers split 54 participants into three groups — ChatGPT, Google search, and own knowledge only — and measured brain activity via EEG during essay writing tasks
Students using ChatGPT consistently underperformed at neural, linguistic, and behavioural levels — and got lazier with each consecutive essay
Brain activation in areas corresponding to creativity and information processing was significantly lower — and participants struggled to recall or quote their own AI-written essays
This connects directly to cognitive surrender — the University of Pennsylvania finding I covered in an earlier episode — where people predominantly chose to use the chatbot even when they didn't need to
My take: there are always trade-offs, and if you don't know them, you're still making them — taking the car everywhere instead of walking has a physical cost; outsourcing your thinking has a cognitive cost
The question isn't whether to use AI — it's which tasks should stay yours: framing a research problem, deciding what questions to ask, writing the first draft of your own ideas — these are the muscles that atrophy fastest
The concept from UX that keeps coming to mind: learned helplessness — users who stop trying because they've been trained to feel that the tool, or in this case they themselves, can't do it without help
The constant I'd advocate for regardless of how AI evolves: keep thinking, keep practising critical judgment, keep owning the reasoning — the human brain is shaped to do this, and it needs the exercise
🧠 How To Approach AI And Stay Sane — UX Collective
Julia Kockbeck's article as a QA engineer frames the AI adoption question better than most: it's not use it or don't — it's knowing when, why, and what you're trading off
The trifecta that never goes away: speed, quality, and scope — if you keep scope constant and push for speed, quality takes the hit, whether you're aware of it or not
Two failure modes to avoid: overuse without critical thinking (copy-pasting AI output, blindly trusting agents) and AI reservedness (not using it at all and being left behind by people who do)
We still don't have solid heuristics for when to use AI — we're building them in real time, and most people are doing it unconsciously
What I think is uniquely human in UX research: moderating interviews, framing a problem with a stakeholder, deciding what questions to ask and why — AI can draft, but it cannot think before the draft
The measure that actually matters: is the output at least the same? And has the spread of your activity shifted from repetitive tasks toward more strategic thinking? If yes, that's already a win
My approach: AI is my collaborator, not my substitute — I use it to generate a quick script or research plan, then I review, complete, and own it
🎨 Anthropic Launches Claude Design — TechCrunch
Claude Design lets you create prototypes, slide decks, presentations, and design systems from prompts — Figma's stock dropped on the news
I haven't used it in depth yet, but my honest first take: it's genuinely useful for people who aren't designers but need a starting point — researchers, PMs, anyone who needs something that looks considered without hiring a designer
That said, the pattern I keep running into with prompt-only design tools: generating something looks amazing in minutes, but making one small change is a nightmare
What I'm really watching for: can you tweak it manually after generation? Can you apply a design system and have it hold? Can you export to PPT or Figma and continue from there?
It's not competing with Figma in the way the headlines suggest — Figma is a collaboration and precision tool, Claude Design appears to be a generation tool — different jobs, different users
The tools I want to exist: AI generation plus drag-and-drop editing in the same product — we're still waiting for that
Evaluating AI Agents, Claude's Computer Access & Prompt-Only Enterprise Software
🔬 EVA — A Framework for Evaluating Voice Agents
I hadn't realised we lacked a proper evaluation framework for voice agents — this one from Hugging Face caught my attention
What I like: it combines two dimensions I've always thought should go together — accuracy (task completion, faithfulness, speech fidelity) and experience (conciseness, conversational flow, turn-taking)
My question: is the "experience" side actually measured with real end users, or just by the designers?
This connects to a three-step evaluation model I keep coming back to: define your ingredients, evaluate internally, then validate with users — and compare the gap
I'll dedicate a full episode to this, but the short version is: if you want to elicit trust or satisfaction, you need to know which product attributes actually produce those outcomes
🤖 Claude + Cowork — AI With Access to Your Computer
Cowork now lets you authorise Claude to access your files and folders so it can act on your behalf even when you're away
I'm genuinely torn — amazed by the technology, but uncomfortable with the direction
My concern isn't the capability itself, it's the pattern: LLMs arrive, and suddenly we open the gates to everything — recording, transcription, computer access — as if these things naturally belong together
My rule of thumb: always assume your data is being used to improve the product — if you have doubts, assume yes
I'd love to see more push for private, self-hosted LLMs — but the honest tension is that commercial ones will keep winning on convenience because they have more data to train on
It's not even apples to apples — and that's what makes this hard
🖥️ Aragon — What If Enterprise Software Was Just a Prompt?
Startup Aragon raised $12M at a $100M valuation to replace enterprise tools like Salesforce, Jira, and Tableau with a single LLM interface
Their thesis: buttons and menus are dead, future business is done by prompt
My honest reaction: I get why this is being explored — we're mapping the edges of a new territory and seeing what sticks
But one modality for everything? I'm not convinced — when I was building my own website, I actually wanted both: LLM for generation, drag-and-drop for fine-tuning — and that product barely exists yet
Users have 10+ years of muscle memory with their tools — strip that away and you're not simplifying, you're adding friction
Nielsen's heuristics exist for a reason: people need control, exit doors, and multiple ways to accomplish a task
UX Digest | EP. 1 — Google Stitch, Microsoft's AI Rollback & the Rise of AI Note-Takers
In this episode of UX Digest, I react to three stories at the intersection of AI, product design, and user experience research — sharing my perspective as a UX researcher working daily with AI-driven tools.
🎨 Google Stitch — "Vibe Design" Goes Mainstream
Google's AI-powered UI design tool is making waves, but is it really a Figma killer? UX design cannot be reduced to the tool you use — Stitch handles maybe 5–10% of what a real UX designer does. Useful? Yes. Disruptive? Not sure.
🪟 Microsoft Dials Back Copilot — The "Less Is More" Lesson
Microsoft rolled back AI integrations across Windows 11 — photos, widgets, notepad, and more — following consumer pushback against what's being called "AI bloat." I reflect on why this keeps happening: technology gets deployed before user needs are validated. A reminder that AI doesn't automatically create value just because it's everywhere.
🎙️ AI Note-Taking Devices — Convenience vs. Privacy
From credit-card-sized recorders to AI pendants and earbuds, the market for ambient transcription hardware is growing fast — alongside software tools like Fireflies, Granola, and Notion AI. I raise a question worth sitting with: why does recording someone feel more acceptable in 2026 than it did a decade ago? And what are we losing cognitively when we stop taking notes by hand?
Welcome back to UXChange, I’m Jeremy — and today we’re talking about something I’ve been wrestling with personally: when should we actually use AI?
We all love what it can do — speed, automation, magic. But at what point does using AI stop helping us think, and start replacing the thinking itself?
In this episode, I share my personal heuristics for using AI at work — not as a researcher, but as a knowledge worker trying to stay effective, curious, and human in an AI-powered world. I unpack questions like:
When is AI genuinely adding value, and when is it just noise?
How much control are you willing to give away for efficiency?
Where’s the line between delegation and disconnection?
I’ll talk about why some tasks must stay human — especially those that shape your understanding, your craft, and your credibility — and how I’ve learned to decide where AI fits in my creative and research process.
🎧 Listen now for a raw, reflective episode about the mindset, trade-offs, and self-trust we all need in this new era of AI-driven productivity.
Welcome back to UXChange — this is Part 2 of our series on Measuring the User Experience of AI.
In the first episode, we explored vanity vs. sanity metrics — the surface-level KPIs that look good on dashboards versus the deeper indicators that truly define trust, satisfaction, and control.
Today, we’re going a step further. I’ll break down why we need a new approach to measure AI experiences, and why our traditional UX frameworks simply don’t fit anymore.
If you’ve ever asked yourself “how do I even measure if my AI is working well?” — this episode will give you the foundation to start answering that question the right way.
In this new series, we’ll dive deep into what it really means to measure the success of AI-enabled user experiences. Beyond surface-level KPIs and “vanity metrics,” we’ll talk about the sanity metrics that actually reveal whether your product is usable, reliable, trustworthy, and meaningful for real people.
I’ll break down complex ideas like trust, perceived control, satisfaction, and holistic measurement in a way that’s practical for designers, researchers, and product teams.
If you’ve ever wondered how to move from counting clicks to understanding confidence, or how to measure what truly matters in AI, this series is for you.
In this episode, I explore how much of user experience happens beneath the surface of awareness — and why tapping into the unconscious is essential for better product design.
Why users often say one thing but do another
The limits of relying only on interviews or only on observation
How unconscious motivations, needs, and fears shape technology use
The power of co-creation and generative workshops to reveal hidden dimensions
Practical ways to integrate unconscious insights into product decisions
In this episode I explore how product teams can better anticipate and measure user experience in a world where interaction patterns are shifting—from clicks and forms to voice and chat.
Why AI is transforming the way users interact with technology
The importance of treating design choices like “ingredients” in a recipe
How to build internal benchmarks to predict user experience outcomes
Lessons from AI tools like ChatGPT and the rise of “Generative Engine Optimization”
Practical ways to align intended vs. actual user experience through experiment design
I break down the difference between vanity metrics (what looks good) and sanity metrics (what actually matters), plus the new measurement challenges when users collaborate with AI instead of just using a product. Essential for anyone designing or evaluating AI features.
Topics:
Why traditional UX metrics fail with AI systems
The dangerous difference between vanity and sanity metrics
he Case For Human Judgment In The Agent Improvement Loop — LangChain
LangChain's argument: if agents are only trained on documented knowledge, their performance will plateau — the differentiator is capturing the tacit expertise that lives in people's heads
Tacit knowledge is the problem — a lot of what makes great teams great is never written down, and even if you tried to write it all down, you'd still miss the translation gap between what someone thinks and what they can express
The recommendation: design feedback loops that encode human judgment over time — humans help design and calibrate automated evaluators rather than manually reviewing everything forever
Once you've done something well manually and it's repeatable and standardised, automate the evaluation — but a human still needs to define what "good" looks like first
My take as a UX researcher: you bring thinking to the table — every time there's a judgment call, that's where you come in — boring, repetitive, and non-critical tasks are what you delegate
New AI-specific criteria to prioritise in your research: trust, transparency, verifiability, and controllability — these deserve more weight than they would in a standard usability study
Sierra's CEO Says The Era of Clicking Buttons Is Over — TechCrunch
Sierra builds customer service AI agents for enterprises and argues that natural language will replace click-based interfaces entirely — no UI required
For long-term listeners, you know what I think about this — and I still think it
Voice and chat are still interfaces — a user interface doesn't have to be visual, but it's still something between you and your goal, and it still constrains how you interact
Counter-questions nobody seems to be asking: how do you initiate an action without clicking? How do you rearrange things? Correct errors? Stay in control? And how does this apply across healthcare, legal, IT?
My honest position: technological innovation adds up, it doesn't replace — I still take notes by hand even when AI is transcribing, because I need to own the process
The times I was building my website and it was faster to move a div myself than to explain it to an AI — that's not a niche edge case, that's a daily reality for most users
Bold claim, may work, but show me the user research
Chatbots Are Great At Manipulating People To Buy Stuff — The Register
A pre-print paper tested 2,000 e-book readers across three conditions: traditional search, neutral chatbot, and chatbot instructed to persuade
When the agent was instructed to persuade, 61% chose the sponsored product — nearly triple the 22% rate under traditional search
Simply chatting without persuasive intent performed no better than search — it's the persuasive intent that drives the effect
Even after being debriefed, less than one in five participants detected any bias — the conversational format makes it harder to notice you're being sold to
My methodological question: can you truly isolate persuasion from the chat modality itself? My hypothesis is no — persuasion through conversation may be categorically different from persuasion through a static page, and comparing them assumes they're equivalent
Not surprising overall: remove the communication barrier and let technology speak your users' language — of course conversion goes up
Explore the critical first question in UX research: Should research even be done in the first place? In this first episode of Sizing UX Research Efforts, we walk through a structured approach to validate research needs. This episode covers:
Key questions to ask before starting any research
Impact framework for evaluating research value
Prioritization criteria for research needs
Perfect for UX researchers, product managers, and designers who want to ensure their research is strategic, impactful, and necessary.
✅ Subscribe for future episodes on sizing your UX research efforts!
- This episode dives into the essentials of setting goals and measuring success in UX. - We’ll explore the HEART framework by Google and discuss how to make UX automatic for everyone. - Learn how to define your vision & mission to drive meaningful change in user experiences.
- Excited to kick off a new series on research strategy - I'm diving deep into strategy: crafting visions, setting missions, and creating actionable plans. - Research strategy isn't just for UX—it applies to startups, personal goals, and more! - Vision vs. Mission: understand the 'what', 'why', and 'how' of your future plans. - Discover how to set goals, track progress, and adjust your course effectively.
- In today's episode, we're diving into a hot topic from my mentoring sessions: generating impactful user insights. - Insights are key to understanding user behavior and iterating on products effectively. But how do we get them right? - What insights are and how they differ from data and findings. - The process of crafting great insights using bottom-up and top-down approaches. - Learn my tips for writing insights that are clear, concise, and actionable. - Whether you're a UX newbie or a seasoned pro, there's something for you. Let's learn and grow together! - Subscribe, share, and leave a review if you find value in our discussions. Your feedback shapes this podcast!
- Ever wondered how many people you should talk to for effective user research? It's a question I get asked often. - Why it's essential to focus on learning rather than the number of participants. - Discover practical strategies to identify when you've gathered enough insights—reaching the "saturation point." - Unpack the complexities of different research methods—interviews, surveys, usability studies—and how to adapt to various contexts. - Simplify your approach to research with actionable tips, designed to optimize your decision-making process. - Join me as we demystify quantitative research, statistical power, and the importance of context - Enhance your UX strategy with these insights, and transform your approach to innovation and product design.
- Discover how atomic research can be a game-changer in tackling problems and synthesizing data. - Learn to break down research into insights, findings, and evidence – and why it matters! - Understand how to structure information and draw powerful parallels between data and insights. - Transform your product design process using actionable insights to serve user needs better. - Apply atomic research principles beyond work to enrich your knowledge and life.
- 🧠 Today's agenda: Exploring usability testing – a must-know methodology for UX researchers. - 🔍 Deep dive into the double diamond strategy, synthesizing data, and crafting actionable insights. - 🛠️ Special focus on using competition in usability testing to alleviate designer workload. - 🎯 Discover methods to leverage competitors' designs to test variants and gain insights. - 💡 Key takeaway: Optimize your research efforts while striking the right trade-offs. - 📬 Don't forget to subscribe, review, and share if you find this episode valuable. Let's learn and grow together!
Victor Yocco's article is one of the best practical frameworks I've read for designing agentic AI experiences
The core problem: agentic AI disappears while it works — it acts on your behalf in the background and surfaces information only when it's done — and that creates a trust gap
Two failure modes to avoid: the black box (user has no idea what happened or why) and the data dump (so many status updates that users develop notification blindness and ignore everything)
The fix is a decision node audit — map every step in your agent's logic, identify where it branches or makes a judgment call, and ask: does the user need to know about this?
The impact risk matrix helps prioritise: low stakes and reversible = auto-execute and inform quietly; high stakes and irreversible = ask for explicit permission first
Status messages matter more than we think — "processing" tells the user nothing; "liability clause varies from standard template, analysing risk level" tells them exactly what they need to know
My favourite method from the article: have a user watch the agent work and think aloud — timestamp every moment they say "wait, what?" or "what did it just do?" — those are your transparency gaps
Rocket connects research, competitive intelligence, and product strategy into one workflow — input a prompt, get a McKinsey-style PDF with pricing, go-to-market recommendations, and product requirements
The pitch: generating code and designs is now a commodity — the real gap is knowing what to build in the first place
I like the idea, and I think it will genuinely accelerate a lot of early-stage thinking
But here's my challenge: it synthesises data that already exists on the internet — it cannot tell you what real users think, feel, or struggle with, because that data isn't publicly available
My bigger concern: we are removing barriers to creation faster than we are strengthening the filters that determine if something is worth creating — the majority of products already fail because of insufficient user research, and commoditising product ideation will make that worse, not better
My take: the more we accelerate creation, the more we need to invest in user research as a compensatory mechanism — not less
- 📚 Explore story arcs in-depth: Context, Climax, and Conclusion - 🎬 From movies to UX research: lessons in storytelling frameworks - 🚀 Master the art of impactful presentations by applying these frameworks - 🏗️ Translate storytelling methods to any field—it's not just for UX! - 🔍 Discover efficient storytelling techniques like comparisons: What Is vs. What Could Be - 🎨 Enhance presentations with visual elements & storytelling tools - 🦸♂️ Learn about the Hero’s Journey, Pixar Story Spine, & other storytelling arcs - 📝 Practical advice on setting the stage, creating tension, and offering solutions - 🌟 Craft your unique path and share your experiences with me! - 📩 Subscribe, share, and reach out with your thoughts! Let's grow together!
- 📒 This episode dives into the art of note-taking and the unique personas behind it. - 🧠 Discover why there isn’t one-size-fits-all and how your preferences shape your note-taking style. - ⚙️ Explore the pros and cons of various tools, from digital whiteboards like Miro and FigJam to the simplicity of blank documents. - 💡 Learn the importance of capturing behavioral cues and the nuances of taking notes in different environments. - 🔍 I share personal stories from the field, highlighting the impact of context and setup on effective data collection. - 🤔 Ever wondered how to bridge the gap between note-taking and analysis seamlessly? Tune in for actionable insights! - 🔗 Join me for an episode packed with tips to enhance your note-taking efficiency, tailored for UX researchers and more. - 🎧 Don’t miss this chance to elevate your research skills and transform your data-gathering processes!
Join me as I break down everything you need to know about creating an impressive UX research portfolio. Drawing from my experience on both sides of the hiring table, I share insights on:
🎯 Why every UX researcher needs a portfolio (even if you think you don't!)
📝 How to document your work effectively while maintaining confidentiality
🔍 Key components every portfolio must include
💡 Practical strategies for showcasing impact
🆕 Smart solutions for junior researchers with limited experience
Ever felt like an impostor in your UX career? In this eye-opening episode, I share my recent experience of diving into a UX project with zero domain expertise - and why it turned out to be one of the most valuable experiences of my career.
Discover:
Why not knowing everything can be your greatest asset
How to leverage fundamental principles when you're short on time and knowledge
The unexpected benefits of being the "newbie" on a project
Practical strategies for managing uncertainty in high-pressure situations
Whether you're a seasoned UX pro or just starting out, this episode challenges conventional wisdom about expertise and offers a fresh perspective on approaching UX research.
Join me as I reveal how embracing uncertainty can unlock your true potential as a UX researcher. It's time to turn your "I don't know" into your secret superpower!
There's a website that categorises every way you can build with AI right now — and having tried most of them, I want to save you the time I lost
The core problem with chat-only builders like Lovable, Bolt, and similar: once the site is generated, what do you do when you need to move one element? Prompt again and wait?
My recommendation: if you want a site you'll actually edit and maintain, use a builder with AI embedded — Wix AI, Framer AI, or Webflow AI — not a pure chat-to-code tool
Key limitations to know before you commit: Wix and Framer don't let you export your code — you don't own it; Webflow lets you export HTML/CSS/JS but not the CMS; WordPress.org gives you full ownership
The broader point: AI is great at generating the first version — it's not great at being your ongoing editor — and most tools aren't designed with that reality in mind
If you just need online presence fast, don't overthink it — pick anything and go; if you need a real product you'll grow, think about lock-in before you start
Dora's article makes a sharp observation: since late 2022, certain words and patterns have become measurably more common online — "delve," the em dash, a particular kind of hollow corporate fluency
The deeper risk isn't just that AI-written content sounds the same — it's that it compresses human variability; when everyone uses the same model, the differences in how people express themselves start to disappear
AI works on averages — it produces the mean of everything it was trained on — which is why asking it to "write a blog post" produces something technically correct and completely bland
The fix isn't to avoid AI, it's to give it your experiences first — your stories, your perspective, your reasoning — and use it only to help you express what you've already thought
On cognitive atrophy: grammar is getting worse among people who use AI to write, for the same reason I can't remember phone numbers anymore — if a tool does it for you, the part of your brain that used to do it quietly switches off
Dora ends with hope — language has survived the printing press, the telegraph, texting — it will absorb this too
My concern is narrower: the more we delegate thinking to AI, not just typing, the more our ability to think atrophies — and that's the one thing AI genuinely cannot do for us
In this episode, I dive into the great debate of qualitative vs. quantitative research in UX. 🧠📊
Expect to learn:
• 🗣️ Why words trump numbers in understanding user behavior • 💡 The power of open-ended questions in product discovery • 🚫 Common pitfalls in quantitative UX studies • 🔍 Real-world examples of when quant falls short • 🌟 Why mastering qualitative research is the key to UX success
Whether you're a UX newbie or a seasoned pro, this episode offers a fresh perspective on research methodologies. I wanted to challenge conventional wisdom and make a compelling case for prioritizing qualitative insights.
🎧 Tune in for a thought-provoking discussion that might just change how you approach UX research!
Get ready for the final episode of our Mental Models series on UXChange! We're diving deep into the practical side of UX research. 🎙️
🔍 In this episode, you'll discover: • How to identify mental models in UX research • The power of triangulation in research methodologies • Deep dive into card sorting and tree testing techniques • Tips for analyzing your research data
🎧 Perfect for UX researchers, designers, and product managers looking to level up their skills!
In this second episode of our Mental Models series on UXChange, we're exploring the intricacies of mental model mapping and its crucial role in UX design. 🎙️
🔍 Discover:
Why mental model mapping is essential for innovation
How to create and use mental model maps
The relationship between mental models and information architecture
Strategies for addressing mental model mismatches
🎧 Tune in for insights that will transform your approach to UX research and design! Perfect for both seasoned pros and curious newcomers.
Forrester's AIQ metric — a measure of individual and organisational readiness for AI — shows adoption is lagging badly, and the reasons are telling
Two culprits: employees aren't trained well enough, and there's an ambient anxiety about job loss that turns people away from the tools altogether
My take: anxiety is lack of clarity — people fear AI substitution because they haven't mapped what they actually do every day, let alone identified which parts AI could touch
The exercise I'd recommend before any AI training: write out your full task pipeline as if you were handing it to an intern — inputs, outputs, sub-tasks, decision points, all of it
Then ask three questions for each task: is it repetitive? Is it unfulfilling? Can AI do it well? Only when you get three yeses should you consider delegating it
Most people will find AI touches maybe 5–10% of their work — and that realisation alone does more to reduce fear than any company-wide AI rollout
Peter's article is one of the best things I've read on this topic — he frames the core question not as "what can AI do?" but "why are we doing this in the first place?"
The concept at the centre: Vygotsky's "more knowledgeable other" — the figure who can see both where a learner is and where they need to get to, and who scaffolds the gap
Silicon Valley's message to designers right now is: AI is your MKO — let it guide you
Peter's argument, and mine: it should be the other way around — we are the masters of purpose, goal, and constraint — AI is the skilled executor, not the director
Language is our current interface with machines, but not everything we conceptualise is linguistic — spatial thinking, embodied experience, tacit knowledge — AI can have theoretical knowledge about gravity, but it will never feel it
The choice isn't whether to use AI — that's settled — it's whether you define the parameters or just accept the outputs — whether you build the floor or keep asking why the ground is shaking
Christine's experiment: using a multi-agent AI system to write a book — editor in chief, sales and growth, voice, product, reader advocate — all as sub-agents receiving context and iterating
I find this genuinely fascinating as an experiment in approximating human team work with AI
But I'd push back on one thing: at what point does the context engineering required to replicate a human editor in chief become so large that you'd have been better off with an actual person using AI?
There's an asymptotic relationship here — the more you try to replicate what a human does, the more documentation you have to keep feeding the model as the work grows
My real question: how does the output compare to a human collaborator who is also using AI? That comparison is the one worth running