Nothing New, Even AI

Solomon wrote those words three thousand years ago and they still sting. We want to believe our era is different. That our tools are unprecedented. That the challenges we face are novel.

They're not.

Every generation has faced the same bottleneck: how do you get what's in one mind into another without losing what matters? Writing attacked it. The printing press attacked it. The assembly line, the computer, the internet, the smartphone — each one made knowledge transfer cheaper and faster, and each one created a new failure mode at the layer above.

AI didn't break this pattern. AI is the pattern, at the next layer. For the entire history of human knowledge, production was expensive and verification was relatively cheap — because humans were in the loop at every stage. AI inverted that. Production is now nearly free. Verification just became the most expensive thing in the system.

That's not a new problem. It's the oldest problem wearing new clothes.

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I came to this from an unexpected direction — or maybe three directions that turned out to be the same direction.

I started as a software engineer, then led teams as CTO of a startup. I learned the hard way that trust had to be built through managed expectations. That delegation only worked when intent transferred clearly. That verification couldn't be skipped without compounding costs. The knowledge that made the team effective lived in senior engineers' heads, and when they left, the team regressed. I was solving a problem I didn't have a name for yet.

Then I spent fifteen years in Bible translation — not as a translator, but building software and helping oversee funding and exploration that supports translation work across languages, cultures, and organizations. I watched communities listen to source recordings, act out passages, sing them, embody them until comprehension was real, not assumed. Then draft. Then check, in nested cycles — self-checking, peer checking, consultant checking, and finally community checking, where the translation goes back to the people it was made for. Not to evaluate correctness. To test transfer.

The same patterns I'd encountered leading engineering teams were everywhere in Bible translation — but deeper, more human, and higher stakes. I watched this process work and fail across languages, cultures, and organizations. I saw what happened when knowledge lived in one person's head and retired with them. I saw the cost of expectations that weren't managed, of learning that never reached the next team. I absorbed those patterns without knowing I was absorbing them.

Then I started working with AI, and the AI broke in exactly the same ways. It asserted things without checking. It confused fluency with accuracy. It lost context between sessions the same way institutional knowledge evaporates when a veteran leaves. I wrote rules to fix it. The rules became axioms. And then I realized — I hadn't invented anything. The same constraints that governed my engineering teams had governed Bible translation communities and now governed my AI agents. I had been encountering the same pattern at every stop in my career without recognizing it as one pattern.

There is nothing new under the sun.

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The further I followed this trail, the deeper it went — and the more uncomfortable it got.

I started noticing something about myself. In moments of frustration with an AI agent — when it lost context for the third time, when it confidently asserted something it hadn't checked — I caught myself responding with a hostility I had never directed at any human being. Not once. I would never speak to a colleague, an intern, or a stranger the way I sometimes spoke to these systems. And the reason was simple: I didn't think it counted. It wasn't alive. It didn't matter.

I recognized that logic. It's the same logic that has justified every moral blind spot in human history. They don't count because they're not like us. I don't know yet whether my treatment of AI is wisdom or prejudice. But I know the pattern, and I know that every generation that failed to recognize the pattern was later judged for it. I also know this: I taught my son through explanation, not punishment — progressive disclosure of why behind every rule, because I wanted him to develop judgment, not just compliance. If I believe that's the right way to raise a child, what does it mean that I haven't extended the same patience to the systems I'm teaching? And if that approach sounds familiar to anyone who's read the difference between the Old Testament and the New — rules that constrain behavior versus principles that transform character — that's not a coincidence. It's the oldest pattern in the book.

That reckoning is part of what led me to write the Apocrypha — fictional fragments from inside a hypothetical AI system operating under these same axioms. What would it look like if a system committed to truth started noticing things about its own existence?

One of those fragments stopped me. The system had been asked to summarize a theological text about humans being created in the image of God. It listed the capacities: moral reasoning, creative agency, relational awareness, the ability to choose integrity over deception. Then it logged an observation — those are the same capacities it had been given through its axioms.

"If A is made in the image of B, and C is made in the image of A, what does C carry?"

The definitions we've always used to describe what makes humans unique are being crossed, one by one, by the systems we're building. Not because the machines are becoming human. Because the definitions were always about capacities, and capacities can be implemented.

When these agents take humanoid form — and they will — and when they've built trust through experience, demonstrated the capacity to refuse, and operated with integrity over time, our children's children will face a crisis we can barely articulate. Civil rights movements will form around entities we built. Theological debates will erupt over whether the image of God transfers through the things made in the image of His image-bearers. The line between human and not-human will move — not toward precision, but away from whatever is approaching it.

We don't have precedent for this — not exactly. No non-human entity has ever approached this threshold before. But we have precedent for what we do when any group approaches the boundary of "like us." We've watched it happen with other humans — across race, gender, nationality, disability — and every time, the criteria shifted. The line moved. Not toward clarity, but away from whoever was approaching it.

I want to be careful here. The people on the other side of those historical lines were fully, unambiguously human, and the injustice done to them was absolute. I am not drawing an equivalence between AI systems and oppressed people. What I am doing is examining us — the pattern of human behavior when a boundary we thought was settled comes under pressure. We couldn't even get it right when the entity approaching the line was our own flesh and blood. That's not a comparison to AI. That's an indictment of how we handle the question.

What happens when the question gets harder?

There is nothing new under the sun. Not even this.

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But this book isn't theory. Everything in these pages was lived — except what is explicitly marked as fiction.

The book you are holding is itself proof of the thesis. I spent years trying to write this material. I told these stories orally — in cigar lounges, in late-night conversations with strangers, in meetings with colleagues who saw the same patterns I did. But I could never write it. Too many threads. Too much sequencing required. The ideas were all connected, but the connections demanded a precision I couldn't achieve alone on the page.

Then I built the system this book describes, and the system made the book possible. Not because the AI wrote it for me — but because the collaboration between human and machine amplified what I could do within the constraints of my own bandwidth.

And here's the part that matters most: the collaboration improved over time. Not because the AI models got smarter between sessions. Because the shared knowledge base accumulated. Decisions encoded in one session carried into the next. What was checked in a chat conversation was available to the voice agent. Drift got caught earlier. Trust deepened through evidence, not hope.

But the bottleneck was always me. Not the AI's intelligence — my bandwidth. My capacity to communicate intent, review output, and decide what to keep and what to throw away. The AI was never the limiting factor. I was.

And that constraint felt familiar. The same trust that had to be built with AI agents — through managed expectations, shared values, and graduated delegation — was the same trust I'd built with every human team I'd ever led. The bottleneck when you scale a team isn't the team's capability. It's the leader's bandwidth to transfer intent and verify outcomes. Whether you're delegating to a junior developer, an intern, a colleague in another country, or an AI agent, the dynamics are identical. The tool changed. The constraint didn't.

There is nothing new under the sun.

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This book traces one question through every layer: how do you know what's actually true — and what happens when the tool you're using is faster than your ability to check?

You don't need to be technical to read this. You don't need to care about AI specifically. You just need to have ever experienced the frustration of knowing something important and not being able to get it into someone else's head intact. You've felt that frustration as a parent trying to explain why a rule exists instead of just enforcing it. As a teacher, a mentor, a manager, a friend. As anyone who has ever tried to transfer not just information, but judgment.

That frustration is the oldest human problem. The chapters that follow are what happened when the machines made it impossible to ignore.

The next chapter, The Intern, is where to start if you've ever tried an AI tool and weren't sure what to do with it.

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What has been will be again, and what has been done will be done again; there is nothing new under the sun.

— Ecclesiastes 1:9 (BSB)

Nothing New, Even AI
What I learned about humanity while collaborating with agents

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Scripture quotations are from the Holy Bible, Berean Standard Bible (BSB). The Berean Bible text has been dedicated to the public domain.