They were successful enough that Microsoft has built grammar checking into Word since 1992. Grammarly alone has 30 million daily users.

For forty years, the message has been clear: use the tools, improve your writing.

Now a student in Palo Alto is staring down a C on his transcript because an AI detector flagged his essay. His family submitted over a thousand pages of evidence… drafts, timestamps, full Google Doc revision history. The district’s response was “we can’t resolve this” so the student pays the price.

The detector’s own maker admits to a +/- 15% margin of error. Independent researchers have shown these tools flag non-native English speakers at higher rates (likely because they’re working harder to master the rules). Grammarly use alone can trigger a positive. I’ve seen it in my own tests!

But the problem goes deeper than bad tooling. AI writing models were trained on good human writing. They learned to mimic it. Which means the better you write (whether you use assistance tools or not) the more you look like a language model. If you’ve learned to write competent, clean, well-structured prose, you are now statistically indistinguishable from the thing we’re trying to detect.

The detectors aren’t broken. The premise is. We trained AI to write like skilled humans, then built tools to catch skilled humans writing like AI. That’s not a technology gap waiting to be closed. It’s a circle.

We either use the tools or we don’t. This half-a**ed middle ground where students, teachers, and families all get caught in the crossfire helps no one.

Data Has Black Holes Too: Why “hallucination” is the wrong word for AI’s deepest failure mode.

The AI industry calls every wrong answer a “hallucination.” That word is hiding a much bigger problem.

When a model fabricates a seahorse emoji, that’s obvious and fixable. When a model produces a confident, well-structured answer that’s wrong because of assumptions buried in the training data it was never designed to question, that’s something else entirely. That’s structural. And nobody’s talking about it in the right terms.

I fed the same degraded 1957 film image to three frontier models. All three independently produced WWII propaganda. The only correct result came after I supplied the actual movie context up front.

The new essay is about what’s really happening inside these systems, why your 500-word prompts are fighting a losing battle against gravity, and how to stop fighting the landscape and start navigating it.

(Read, The Room Where It Gets Built — Essay #9: Data Has Black Holes Too)
 

You walk into a store. The price tag makes no sense. “Who’s paying for this?!?”

Not you. The store did the math. They figured out they don’t need you. They don’t need ten of you. They need one of a different customer who pays sticker and doesn’t blink.

I wrote an essay about how this same dynamic is playing out across the entire hardware market and most people haven’t noticed. About what happens when data center equipment comes off cycle in three to five years. And about why even a flood of cheap enterprise surplus might not help you, because the consumer operating system is being redesigned to lock you out of it.

Thirty years of pattern recognition on this one.

(Read, The Room Where It Gets Built — Essay #6: You Are a Decimal Point)
 

This is a transitional moment in time. We have introduced the world to narrow AI through LLMs and presented it as the final solution to our creative needs. It’s a leveling moment where anyone regardless of their skill in writing, art, programming can now become as capable as the average of us gifted with talent. Great artists will use AI to elevate their work. Art will go in new and unexpected directions. But what I want to talk about is the fallout.

(Read, The Room Where It Gets Built — Essay #1: Luddite)