The process is the point: on asymmetric workfare, workslop, and preserving our skills

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The process is the point: on asymmetric workfare, workslop, and preserving our skills<br>What to do manually, even when you have AI

finn<br>Jul 25, 2026

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The Flower Makers, Johann Hamza<br>Using AI to write often feels like cheating. This is most obvious in education: a history teacher doesn’t assign an essay exploring the root causes of the French Revolution because they really want an eighth-graders’ opinions on Robespierre. That essay is a forcing function to revisit material, do research, and reap the benefits of putting ideas into words. AI can write a pretty good (better than an eighth-grader) essay on the topic instantly, without the hassle of research, revisiting materials, or editing.<br>But the benefit isn’t in the final essay, but in the process it took to write it. This is obvious in education, but also applies at work.<br>Thanks for reading Finding Trust! Subscribe for free to receive new posts and support my work.

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At a past company, we needed to revamp a part of our growth strategy (the team I work on). We had three AI-generated strategy documents from three people that were shared once and forgotten. None went through comments and revisions to create an official version that could act as shared context. The desire to “adopt AI” and have some version of a strategy negated any benefit a strategy document may have.<br>At work and elsewhere, some documents or other artifacts produce their benefit because they exist while others are valuable because they’re evidence of the process that created them. The former is a perfect target for LLMs while the latter causes more harm than hurt when done with AI.<br>I work in growth/marketing, so my examples are about writing, but this maps to every profession in upheaval due to LLMs, like software engineering, design, and much more.<br>The easiest way to explain when the effort is the point is in relationships.<br>Why flowers are meaningful

Rory Sutherland believes one reason women love receiving flowers from men is because most men would never buy flowers for themselves. Flowers are proof he was thinking of making her happy. As Miley Cyrus would have us know, she could’ve bought herself those flowers. But the fact that he made that effort makes the flowers meaningful.<br>Flowers are one stereotypical example of a broader point. It may as well be a woman buying her husband flowers, or one partner planning a surprise dinner at a restaurant they dislike but their partner loves, or a friend remembering the wine they shared and buying that same bottle when they reunite.<br>The flowers, restaurant, or wine are symbols for the fact that one person spent time to make the other happy. They’re a byproduct, meaningful because of what they symbolize. This is when we imagine buying flowers you forgot your partner was allergic to: those flowers are suddenly evidence how little you care.<br>These are intuitive examples because interpersonal relationships don’t exist to complete deliverables, run workflows, and achieve objective results. Work is more transactional.<br>AI hands us the results of once-laborious processes and makes many processes much faster this is great because nobody misses taking meeting notes or scrolling through a recipe blog tracing the history of tomato sauce back to the Roman Empire before reading a basic recipe.<br>But using LLMs becomes a problem when we use them to generate outputs whose value lies in the process they prove.<br>How workslop hurts us all

What makes a strategy doc, project plan, or design spec valuable? Clear responsibilities, simple instructions, and well-defined milestones are nice characteristics, but the benefit lies in the process of a shared cognitive context.<br>Before LLMs, arriving at an official strategy document went through important processes:<br>The author wrote it from scratch, with the typical benefits of the writing process: understanding (and plugging) the gaps in one’s knowledge and sharpening one’s understanding by explaining it to others.

Review cycles surfaced where perspectives collided and required reconciling the differences into a shared perspective until there was a final version.

This process makes foundational documents useful because people understand not just what to do, but the underlying thinking. Their approach to tasks will reflect that and they can explain it to others. Meetings are smoother because everyone shares the same context.<br>Even if you could’ve generated the same doc to the letter with an LLM, the outcome would’ve been worse because nobody did the process it should represent. AI accelerated creating the document, but then creates friction in everyday work. The lack of a shared context causes more questions to come up and reviews necessitate starting from scratch. Outcomes are worse if people make faulty assumptions or take longer because the back and forth starts when tasks are assigned.<br>I’ve had...