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?

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)
 

AI is the latest advancement in automation. The job losses, the industry shifts, the civil upheaval. None of this is new folks, but that also doesn’t make it fun or exciting to be the one replaced. I’ve been on both sides of this, trust me I feel your pain.

Humans like to make work easier and more efficient. Are there still people working in some of these industries? Sure but not at the scale of their peak when these jobs would have been a career choice. Let’s just go back say 150 years…

The steam-powered drill replaced human miners (and yes, John Henry beat it once, and it killed him)

The gas-powered tractor replaced significant human and animal labor

The moving assembly line and subsequent robotics replaced the skilled factory worker

Various waves of agricultural harvesting automations have reduced the use of manual field labor (from cotton to strawberries)

(Automatic) Computers replaced human “Computers” wiping out an entire career staffed primarily by women

ATMs have replaced bank tellers

Automatic telephone switchboards eliminated an entire career path

Spreadsheets replaced formal Bookkeepers and Accounting Clerks

Desktop publishing wiped out the prepress industry

Online hotel and travel reservation booking has replaced the travel agent

And for the average person on LinkedIn, this is probably more personal than previous waves of automation because it reaches into knowledge work, creative work, and professional identity in ways people didn’t expect. For the last 40 years we told people those careers were safe.

And unless you’ve been raging against tractors, ATMs, spreadsheets, online booking, desktop publishing, industrial robotics, and every other labor-saving tool with the same energy, then maybe this isn’t really a principled objection. That’s not consolation to you or those who came before you though, is it?

Do I have an answer? No. But sci-fi writers have been proposing them for decades: universal basic income, radical restructuring of how we think about work and value, decoupling survival from employment. The ideas aren’t new. We just refuse to take them seriously until the crisis is personal. And even then, we’d rather fight about whether the automation is fair than talk about what comes after it.

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.