Automation, AI, Scenarios: ID Links 6/9/26

As I read online, I bookmark resources I find interesting and useful. I share these links about once a month here on my blog. This post includes links related to automation, AI, scenarios, and visual inspiration.

Automation

After Automation | Every

If you’ve ever spent time cleaning up an AI-generated draft, you’ll get the initial point here: AI creates more work for humans, not less. But this article digs deeper into how working with AI, especially AI agents, changes the nature of work. Employees at this author’s company spend more time directing agents (deciding the goal and what “good” looks like) and on judging the results of AI. The easy work that AI can do gets commoditized, but the hard work of taste and judgement become more important.

There’s no tipping point coming where things flip and the jobs are gone. The new reality is the opposite—the more we automate, the more expert human work there is to do. Here’s why: AI commoditizes the residue of human expertise—whatever can be made explicit enough to train on. That collapses the value of default model output and creates demand for what’s different. Demand for what’s different is demand for human experts, even as we approach artificial general intelligence (AGI).

Across both forms—coworker and embedded—the pattern is the same. Employee agents take over more of the stable, repeatable, well-framed layer of work. But there is a lot of work that still requires a human being in the loop. We’ve found over and over that for any kind of complex task, the best way to get great work is to have an AI and a human going back and forth in the same workspace.

In every example, the agent needs a human in order for the work to, well, work. Someone has to point it at the right thing, decide whether the output is good, catch the places where it is wrong, and turn the result into a real-life decision or process. The further away an agent gets from a human who is in charge of making sure it works well, the less well it works.

When work is abundant and looks alike everywhere, the work that doesn’t fit the pattern becomes the rare, valuable, and high-status thing(5).

This is why, in practice, AI does not eliminate expert human knowledge work. It dramatically increases the volume of work being done, and none of that work is differentiated or valuable unless a human being is involved.

—Dan Shipper

AI Brain Fry, Workslop and the Ironies of Automation

This is a long article, but worth spending some time to digest. One of the ways that AI changes the nature of work is by increasing the amount of time we spend in cognitively challenging tasks like evaluation. But human brains need breaks and variety.

What remains after automation is not a simplified role but an arbitrary residue of the most demanding, most ambiguous, and least supported work in the entire system. The human is not replaced. In other words, the human is paradoxically left with the hardest parts, and given almost no preparation for them.

Surveying nearly fifteen hundred full-time workers across industries, roles, and seniority levels, the researchers found that intensive oversight of AI tools was the single most mentally taxing form of engagement their participants described. Workers required to monitor AI agents closely reported fourteen percent more mental effort, twelve percent more mental fatigue, and nineteen percent greater information overload than those whose AI engagement was less demanding.

The final irony of automation, she wrote, is that the most successful automated systems, those with the rarest need for human intervention, are precisely the systems that require the greatest investment in human skill.

—Carl Hendrick

AI

AI and Copyright: What You Can and Can’t Do with AI-Generated Content (2026)

An overview of AI and copyright. Who owns it? When can you use it for commercial purposes? How can you protect your AI-assisted work?

Image AI prompts

A large collection of images generated in Nano Banana, ChatGPT, and Seedream plus their prompts. Seeing how others have prompted for images is helpful in figuring out what works and what doesn’t.

AI Effectiveness Rating

Christopher Lind’s simulation tool for rating effectiveness working with AI

Oboe

Ask this AI about a topic and get a lesson on it. This is for self study, and it seems potentially useful for basic topics where there’s a lot of high-quality publicly available information to draw from. You can generate flash cards or study guides too. This might be something that’s more useful for students in school, but I can see it for employees wanting to learn the basics of visual design, copywriting, etc.

Scenarios

New Scenario-Question Generator — Version 5 » Work-Learning Research

Will Thalheimer released an updated version of his template for generating scenario questions. This includes notes about differentiating people who know how to do a task successfully and those who don’t know. The template also includes an annotated example scenario.

Clinical Case Study: Diane

This is a short scenario built in 7Taps by IDLance to use as spaced repetition to reinforce prior training. One aspect I really liked in this was the interaction where you listen to two people’s arguments for different courses of action. Then, you have to decide which is the better way forward. This technique could help make binary choices feel more realistic in the context of a scenario, especially if the justification for the worse answer is plausible for how people think.

Visual inspiration

21 Ways To Get Visual Ideas

Connie Malamed recently published a significant update to her article with resources for getting visual ideas and inspiration. This includes so many links to visual resources across the 21 categories.

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