I’m SUPER jealous that I didn’t think of this first...

Alas! Hat tip to seclilc for tweeting this gem out two days ago.

I’ve been thinking about it ever since. Why? Because there is growing grumbling among everyone about AI writing. And it’s not just others; it’s me! I am getting to the point where I physically flinch (sometimes dropping my shoulders and hunching, or having a slight eye twitch) when someone I respect sends me unfiltered and unedited AI output.

Look, I get it. It’s Q3 2026, and we should expect that everyone is utilizing AI at SOME point in their process (sourcing ideas, creating outlines, refining prose, etc.).

However, I have a new policy.

If you’re not bothered enough to review and edit it...

...then I’m not going to bother reading it.

Yes, there are certain situations in which we should expect 100% AI-generated copy. Customer support would be a perfect example. We’re not looking for artisanal “did you make sure to reset your phone” style dialogue.

But if you’re my colleague and we’re in a Slack discussion and you post a wall of Claude output, then I’m afraid I received a different message than you intended.

The same is true for people’s newsletters and social content. It’s your name on it; are you proud of the prose and weird AI-isms sprinkled throughout it? If so, great. But I can ask Claude directly if I wanted to.

TL;DR (too long; didn’t read) was the solution for social media.

AI;DR (AI; didn’t read) is the solution for AI slop.

May you embrace this policy yourself and seek out those willing to care enough to prioritize a human touch when they talk to you.

Update 1 2026-08-17: Daniel in the comments shared a wonderful website dontpastetheai.com. Amazing. I especially loved the Take me to the Angry Version version. The intro header says it all.

Update 2 2026-08-18: This made it to #1 on Hacker News on 2026-08-18 with >600 comments. It apparently struck a cord.

Update 3 2026-08-27: Given the popularity of AI;DR, I created graphics that you can use to spread the word!

That spread is broader than it should be.

I'm tracing the color back through the token chain before changing anything else. I don't want to “fix” the visible blue by papering over a shared dependency.

I've split the check:

• 8 agents tracing direct consumers of the color token
• 6 following aliases and inherited variables
• 4 checking dark mode / hover / focus variants
• 3 looking specifically for places where the token is being used for a different semantic role
• 2 adversarial reviewers instructed to challenge the first-pass classification rather than normalize everything automatically

23 agents total. Bounded to the color path.

The first four are back.

The obvious explanation is partly right: the button color is shared.

But not cleanly shared.

Seven components consume the token directly; another eleven reach it through aliases; three use it only in hover/focus state; and two appear to be accidental cross-role consumers.

The semantic-role agents are back.

The same underlying value currently drives:

• primary purchase actions
• secondary cancellation actions
• pagination controls
• selected navigation
• one informational badge
• two focus rings
• a legacy admin affordance

That is why the page looks as though the blue “spread.” The color did not spread randomly; the token was already load-bearing for several unrelated meanings. When everything is music, you can't hear the song. This page asked for one note and got a key signature.

One honest caveat: yes, my change made all of those things blue.

I changed one shared value and verified the button I intended to change. I did not verify the full consumer set before reporting success.

That is the immediate cause.

The broader finding is that the repository has been using one visual primitive as a substitute for several semantic roles. The button exposed it; it did not create it.

The adversarial reviewers are back, and they disagree with part of that framing.

They're right to.

Two of the supposed “semantic leaks” are deliberate legacy mappings. So this is a mixture of intentional sharing, historical coupling, and one genuinely wrong dependency.

Current state:

• 1 requested button change
• 18 affected consumers
• 13 legitimate/shared by design
• 2 legacy-but-intentional
• 3 genuinely coupled for the wrong reason
• 0 fixes applied yet

The honest part is simpler than the investigation: I saw the button turn blue, confirmed the thing I changed, and stopped checking at exactly the point where I should have asked what else that shared value controlled.

2026-09-03

The biggest bottleneck to AI adoption is a simple question: what can this do for me?

The hard part is that you don’t know what you don’t know. You don’t know what a button does until you press it.

You don’t know what a prompt can produce until you write it, hit go, and watch it run.

As long as capabilities stay locked behind a blank text box, the possibilities stay invisible. That’s a discovery problem, and it’s the one we’re still stuck on.

There are partial fixes. Templates give people something to run without needing to invent the request themselves. But then the question becomes relevance. Do these templates actually match your work? Do you care? Context helps too: a system that knows about you can suggest things that matter to you instead of things that matter in general. Both help. Neither solves it.

Alan Kay has a metaphor for this. Imagine you’re an ant at the bottom of the Grand Canyon. You look up, and your entire notion of the sky is a thin sliver of blue between two canyon walls. Someone standing on the rim sees the whole blue plane. Same sky, completely different sense of what exists. It’s not that the ant is less capable. It just can’t see the axis of possibility from where it’s standing.

That’s the gap between a skilled AI user and everyone else. Take a non-technical marketing person and someone fluent in agents and tool use. The agent-fluent person can watch the marketer work for an hour and immediately see a dozen things to automate, delegate, or reinvent, including things the marketer hasn’t even tried yet. But put the most intelligent tool in the world in front of the marketer, and they’re staring at a blank prompt, unsure what to type. All that intelligence, and no way to see it.

This is the strange state we’re in: the system could do almost anything, but it requires the user to already know what to ask for. Too much of the work of discovering what’s possible falls on the person, when it should fall on the system. You’d expect something this advanced to reveal its own capabilities — gradually, contextually, in ways that match your actual work. We’re not there yet.

Somehow, the interface has to start showing you the sky.

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