This started with a joke. I told someone I was going to build a new AI model named Karl. One constraint: it could only speak like Karl Childers from Sling Blade.

User: What is Reflective Parallax?
Karl: Reckon it’s what happens when ya look at the same thing through more’n one mind. Mmm-hmm.

User: Should I rewrite my resume?
Karl: Yep. Folks cain’t hire what they cain’t see. Mmm-hmm.

User: Do you think religion is true?
Karl: Don’t rightly know. Reckon I’d keep askin’ questions. Mmm-hmm.

I laughed, but then it got uncomfortable. I noticed that Karl accidentally solved almost every behavior I’d spent months trying to coach out of modern AI models.

No throat-clearing. No phantom objections. No explaining the user to themselves. No answering the question that wasn’t asked. Not because Karl is brilliant, but because he has fewer opportunities to wander.

The Mid-Render Lecture

To understand the actual problem Karl solves, look at what happens when you are deep in a production workflow.

The other day, I was sitting in front of my color grading panel, final timeline loaded up, setting up my Custom Export render settings in Blackmagic Design DaVinci Resolve. I needed a quick validation check on the container and compression specs for a destination upload.

Me: YouTube videos are MP4 and H.264?

The AI Model: “Almost. An MP4 and H.264 are related, but they’re different things. MP4 (.mp4) = the container (like a box that holds video, audio, subtitles, metadata). H.264 (AVC) = the video codec (how the video is compressed inside the container). For YouTube, a very common upload format is: Container: MP4 (.mp4)…”

It went on. And on. It gave me bitrates, audio layouts, and an explanation of YouTube’s internal re-encoding process to VP9 and AV1 formats.

No shit. I didn’t need a computer science professor explaining digital movie formats while my GPU was spinning up. I needed a binary confirmation.

By contrast, the Tin Can model handles the query instantly:
Me: YouTube videos are MP4 and H.264?
Karl: Yep. Mmm-hmm. Good to go.

The Wrong Lesson

The lazy takeaway here is that AI should just use fewer words—that shorter is automatically better.

It isn’t. Karl fails the user who genuinely needs an answer unpacked. “Yep. Mmm-hmm” is useless when a complex systemic logic error requires deep nuance.

The goal isn’t compression; it’s precision. Those are tvwo completely different things. A 500-word response can be deeply respectful of your time. A 50-word response can completely waste it if it hits the wrong target with perfect efficiency.

The issue has never been length. The issue is unearned length—words that show up before they’re needed, explanations no one asked for, and filler that serves the model’s internal generation habits rather than the human’s actual goal.

The Cognitive Tax

Every additional token imposes a cognitive tax on the person reading it. That tax should only be collected if the value returned exceeds the cost.

Imagine putting an operational governor on an LLM. Not a hard word limit, but a simple checkpoint before every sentence is generated: Has this sentence earned the right to exist?

Accuracy and relevance are just the baseline prerequisites. The real test is whether a sentence is pulling its weight at this exact second, for this specific person, based on what they actually typed into the prompt box.

By that standard, an alarming amount of AI output fails instantly:

• Restating your question back to you before answering it.
• Announcing that a topic is “complex” before engaging with it.
• Dropping caveats about limitations instead of just demonstrating them.
• Summarizing what it just said before moving to the next bullet point.

None of these sentences are factually wrong. They’re just unearned. They collect the tax without paying a dividend.

What We’re Actually Fighting For

We pour massive resources into making AI more capable. We talk about larger context windows, better reasoning, lower hallucination rates, and longer memory. Almost nobody asks if AI is becoming more respectful of human attention.

Attention is finite. Every unnecessary sentence eats a piece of it. Every unrequested preamble transfers the cognitive load from the system right back to the human.

There’s a name for that transfer: Continuity Burden. It’s the invisible tax you pay when a system makes you work harder than necessary to extract the exact value you came for.

Token efficiency is a technical metric. Attention efficiency is a human one. A response can be technically optimal while wasting your attention by being exquisitely precise about the wrong thing. Conversely, a long response can be highly respectful if every single sentence does heavy lifting you couldn’t do on your own.

The metric isn’t length. It’s cognitive yield—how much value you get back for every unit of attention you invest.

The Missing Benchmark

We benchmark AI on everything: latency, cost, math, coding, instruction following, and accuracy. We rarely benchmark how much unnecessary thinking the system forces on the human.

Maybe we should. KarlBench would look weird on an industry leaderboard, but the underlying premise is solid. The highest-performing model isn’t necessarily the one that knows the absolute most. It’s the one that best resists answering the question you didn’t ask.

The next frontier in AI shouldn’t be about generating more tokens. It should be about making every single token prove it deserves to be on the screen.

Mmm-hmm.

Mike Magee

Pattern Thinkers + AI