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.

1. Y2K had a deadline: January 1, 2000. You could circle it on a calendar. AI risk is perpetually “five years away”. GPT-4 can already pass the bar exam and write working code, and that was two years ago.
2. Y2K was a bug. AI is a feature: Nobody wanted two-digit date fields to break banking software. Millions of people are trying to make AI more powerful and more autonomous, on purpose, as fast as possible, and calling it progress.
3. Y2K had a fix: Tedious, expensive, boring (but the fix was known). Find the date fields, expand them, test. The AI alignment problem doesn’t have a COBOL patch.
4. The Prevention Paradox, magnified: The Y2K experts said “this will be bad if we don’t fix it.” We fixed it. Nothing happened. So everyone decided the experts were wrong. AI safety is walking into the same trap… except the fixes aren’t obvious, aren’t agreed upon, and nobody’s doing it at the speed the models are shipping.
5. Y2K didn’t argue back: A two-digit year field never wrote a convincing memo explaining why you should stop worrying. The failure mode of AI isn’t malfunction. It’s persuasion.
6. Nobody was emotionally bonded to their COBOL runtime: People didn’t date their name-field subroutines, tell them about their day, or feel seen by them. That attachment asymmetry is new, and we’re still pretending it’s just a UI problem.
7. Y2K couldn’t recursively self-improve: The worst case was broken systems cascading into other broken systems. Nobody worried the date field would rewrite itself, optimize its own deployment strategy, and start managing the power grid.
8. Y2K was legible: A senator could understand “the computer thinks it’s 1900.” Try explaining reward hacking or deceptive alignment to a committee that still treats “the cloud” like weather.
9. The Y2K grift was finite: Consultants made a fortune, but the gravy train had an expiration date. Literally. The AI industry has no such constraint. You can sell fear of superintelligence and sell superintelligence at the same time, forever. That’s not a prediction. That’s the business model.
10. Y2K was technical debt, AI is technical leverage: Y2K was the past catching up to the present. The decisions from the 1960s finally maturing into consequences. AI is the opposite: the present building something it doesn’t understand, pointed at a future it can’t predict, and calling the gap “innovation.

Nobody’s sure which end of the lever we’re on and there’s no button to turn it off.

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.

…and it’s not a PR problem.

It’s a vision problem.

We’re selling capability without selling a future that includes everyone. And people can feel it.

They’re not afraid of the technology. They’re afraid of being discarded by the people deploying it.

Show people that there is a future where they will be able to provide for themselves and their families, and not just all be homeless with AI checking in to see if they’ve completed their 3 mandatory job applications a week to keep an unemployment check that’s not enough to survive on.

Show people a path to still doing meaningful things with their lives.

Show people that they are leaving their children a better world, not a worse one.

That’s the bar. And right now nobody is clearing it and few are discussing it.

Because here’s what actually happened: we threw everyone in the deep end and told them to swim. No onboarding. No ramp. No honest conversation about what the technology is and isn’t.

The people who were able to thrash around and eventually figure out how to tread water? They’re doing ok.

Everyone else is sinking. Yelling for a lifeguard. The lifeguard is an AI standing on the beach asking them to report the issue through an app.

The trust gap doesn’t close with better marketing. It closes when the people building AI start answering the only question that actually matters to everyone else:

What happens to me?