An AI detector just flagged 46% of the Pope’s new encyclical as AI-written. The encyclical is about AI ethics. It was written in a prose tradition over a thousand years old. The same detector rated other paragraphs of the same document at essentially 0%. Same author. Same document.

I ran a similar experiment on myself. I asked ChatGPT to review my personal blog from 2008-2017 and identify posts that read as AI-written. It identified 35% of them as having structured arguments, clean frameworks, numbered examples, and tidy conclusions. None of them were AI-assisted. None of them could have been. ChatGPT didn’t exist yet.

The three worst offenders: a 2009 post about Twitter with definitions and numbered use cases. A 2010 business case for mobile websites with data and a strategic conclusion. A 2014 incident postmortem with a failure chain and lessons learned. Those aren’t AI patterns. Those are writing patterns. Humans have been organizing their thoughts like this for centuries.

A year ago these same tools were being sold to help you write more clearly. Now writing clearly is the evidence you used them.

Even the article covering this story hedges: “practitioners should treat single-detector outputs as suggestive and seek multi-method forensic work before drawing firm conclusions.” Here’s a conclusion that doesn’t require forensic work: if a writing tradition predates electricity, maybe weight the patina of the source before you let an algorithm accuse it of being a machine.

#AIDetection #FalsePositive #WritingIsNotACrime #AIEthics #ContentAuthenticity

Here’s a prompt that costs almost nothing to send and potentially thousands of times more to process:

What is the game that results from when you subtract ‘oof’ from ‘tiny’?”*

That’s about 18 tokens. A rounding error on anyone’s invoice. But to answer it, a model has to attempt letter-by-letter subtraction, realize it doesn’t map cleanly, consider whether it’s a lateral thinking puzzle, try phonetic approaches, evaluate anagram possibilities, backtrack through failed hypotheses, and maybe still get it wrong. The visible output may be one sentence, but the internal search it provokes can be orders of magnitude larger than the prompt.

Meanwhile, pasting a 2,000-word essay with the instruction “fix my typos” is expensive by the meter. But computationally it’s almost trivial. Pattern matching against known English. The model barely has to think.

Token-based billing measures volume of text, not difficulty of processing. It’s like billing a machinist by the weight of the finished part. A titanium watch component weighs almost nothing and costs a fortune to manufacture. A steel doorstop weighs five pounds and takes thirty seconds on the lathe.

The usual defense is that it works on average. Across millions of requests, the riddles and the typo corrections roughly cancel out in aggregate. And that’s probably true. But “works on average” is an actuarial argument, not a logical one. Insurance companies price risk on averages too, and they still get wrecked by correlated tail events.

So the real question isn’t whether token pricing is wrong. It’s whether it creates exploitable asymmetries. If you can systematically construct inputs that maximize compute per token spent, you’ve found the seam in the pricing model. And that seam gets wider as models get better at reasoning, because reasoning is exactly the capability where input complexity and output cost decouple the most.

Nobody’s billing for thinking yet. But thinking is where the cost is.

* See Winning Ways for Your Mathematical Plays (Berlekamp, Conway, Guy) for definitions of “oof” and “tiny.

Why saying it once was never the strategy.

Advertisers, love them or hate them, know their business with product placement and getting press. You want your client or product ever present in the cultural vocabulary or it/they will be forgotten as quickly as yesterday’s breakfast.

Entertainment and media companies understand this dynamic on a different layer. Look at Disney and Nintendo. People complain “why isn’t X making more media for adults.” Because you’re not the audience. You were never the audience short of being a vehicle to introduce your children to the brand via a nostalgia bridge.

There are always going to be new five year olds who haven’t experienced Mickey Mouse and haven’t played a Mario platform game, and those children eventually aren’t interested in Mom and Dad’s crusty old SNES from the cupboard. Their friends are playing the latest Mario Kart on the Switch 2. It’s bright, it’s kinetic, it’s coded to their sensibilities.

Sure, Nintendo makes games for adults, and they do it to keep that console in the house of the twenty-something so when they have kids, the entry point is ready and waiting for the next generation.

Disney plays the same long game in their own right. These companies aren’t repeating themselves. They’re re-presenting to an audience that doesn’t know the product exists yet, with a focused target window that, if they can capture it, wins them the entire household.

Every industry has a version of this. The message isn’t stale. The audience is new.

And tying it to this post. If you haven’t seen what I’ve written before, take a read. This is my re-presenting to an evergreen audience.

You get exactly what you asked for. Not what you meant.

I watch people write prompts like they’re Googling something. A few keywords, a vague direction… Then they’re frustrated when the output is generic, wrong, or just weird.

But the model did exactly what you told it to do… and that’s the problem.

The old genie story works because the genie isn’t malicious… it’s literal. “I wish for a million bucks” and a million male deer appear in your yard. The genie did exactly what you asked it, you just didn’t think about your question.

LLMs operate on the same principle, minus the malice and the deer (usually). When you prompt “write me a marketing email,” you’ve described approximately four billion possible outputs. The model picks one. You hate it. You try again with the same vague prompt. You hate it differently. You conclude the tool doesn’t work.

The tool works fine. You made a genie wish.

What changes everything is realizing that prompt engineering isn’t about clever tricks or magic words (usually). It’s about the same skill that makes someone effective in any leadership role: the ability to articulate what you actually want with enough specificity that another intelligent entity can deliver it.

Tell it who it’s writing for. Tell it what tone. Tell it what success looks like and what failure looks like. Give it an example of something you loved and something you hated. Tell it what to leave out (the negative space is just as important as the positive).

In other words: do the work you should have been doing with your human teams all along.

The uncomfortable truth about prompt engineering is that it isn’t an AI skill. It’s a communication skill. The people who are bad at prompting are usually the same people who send their teams vague Slack messages and then get frustrated when the deliverable misses the mark.

The genie didn’t get it wrong. It followed the rules exactly.

I started writing this series because I needed to air my mind. A career in technology, nothing constant but change, and a compulsion to say something about what I was watching happen.

Ten essays later, this is the last one.

It’s called Dead Reckoning, and it’s about the difference between knowing how to use the instrument and knowing how to read the water when the instrument is incomplete. A Micronesian navigator named Mau Piailug sailed 2,500 miles without a compass in 1976 because he could do both. We’re building an entire industry around people who can only do one.

If you’ve been following along, thank you. If this is your first one, the bar’s been open for a while, and there’s a seat.

(Read, The Room Where It Gets Built — Essay #10: Dead Reckoning)