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.

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)
 

As technology leaders, our most critical job is understanding the actual architecture of the tools we buy. If we evaluate probabilistic AI models using the same procurement mindset we use for enterprise software, we expose our organizations to catastrophic, invisible risks. I looked at the recent DoD/Anthropic negotiations as a case study in how dangerous this category error can be.

(Read, The Room Where It Gets Built — Essay #8: LLMs Are Not Shelf-Stable Products)