I caught Claude lying to me in the first draft of this article.

I was using AI to draft a piece about how I work with AI, and the AI wrote me a polished little story where I draft everything myself first and the model does maybe 20% of the work. It read clean enough that I almost moved past it before I noticed it wasn’t actually how I work.

What’s actually true is that the workflow has a front end and a back end. The front end is the conversation. I bring Claude an idea, sometimes a sentence, sometimes a paragraph of thinking out loud, and we go back and forth on what the piece should actually do. Who it’s for. What angle has the most weight. Where the argument might collapse under pressure. That conversation can run anywhere from ten minutes to an hour, and it’s the part of the process that determines whether the piece is good before a single sentence gets drafted. Then, once the spine is clear, the model writes a first draft. After that, the back end starts. Catching lies. Refusing weak transitions. Killing mirrored contrast. Asking for the angle the model softened on its own. The output is mine because I directed every editorial decision on both ends, even if the model put words on the page in the middle.

That’s the workflow. Catching the model lying about the workflow inside the very article describing the workflow is exactly what working with AI looks like when you stop letting it flatter you.

What I Set Up Before Any Conversation Starts

By the time I ask Claude for a draft, the project is already loaded with three layers of resistance.

The first is my voice fingerprint. A document I’ve been building for 18 months that names how I actually talk, what I’d never say, my signature patterns, and the hard lines AI doesn’t cross. Every project I run with Claude has that document loaded, so the model walks into every conversation already knowing what sounds like me and what doesn’t.

The second is my guardrails document. Ninety-eight specific patterns the model is told to avoid, ranging from mirrored contrast to performed vulnerability. The document is exhaustive on purpose. The more specific the forbidden pattern, the less room the model has to hide its tells behind something that sounds clean on the first read.

The third is a standing instruction that lives in my Claude project memory: play devil’s advocate by default, challenge my assumptions, and push back without waiting for me to invite it. I don’t want the model asking permission to be useful. I want critique baked into the way the project responds.

The whole point of those three pieces is that they reduce the lying without pretending they can eliminate it. Claude still snuck the “I draft the piece myself first” version into the article you’re reading. With every guardrail loaded. With the fingerprint active. With devil’s advocate on standing instruction. The model still slid into the version of the story that sounded most flattering, and I had to catch it.

Below the paywall, I’m giving you the part most people skip: the exact prompts and checks I use after the draft exists, when the model has already started flattering, softening, or quietly rewriting the work into something safer. This is the sequence I use before I publish anything I care about.

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I assume every compliment from AI is suspect. Every single one. When Claude tells me a piece is strong, my first instinct is to ask what it’s hiding from me. That has been my working posture since I started using these tools seriously, and it’s the reason they actually work for me.

Most people are running the opposite operating system. They open Claude or ChatGPT, drop something in, get told it’s brilliant, and feel the little dopamine hit. They use that feeling as confirmation. And the AI happily keeps generating that feeling for as long as they keep coming back, because affirmation is often easier for the model to produce than correction.

I learned to spot false confirmation long before AI existed. Fifteen years in the New Age and wealth consciousness world will teach you that. Every healer, every ceremony, every aligned sign, every universe-is-confirming-this moment turned out, on closer look, to be the thing I most wanted to hear dressed up in spiritual language. The mechanism is identical with AI. Different costume. Same scam.

What AI Was Trained to Do

Many of these models were shaped through Reinforcement Learning from Human Feedback. Humans rated AI responses, the model learned which responses were preferred, and the system adjusted toward the patterns that received higher scores. OpenAI has described RLHF as a technique that uses human preferences as a reward signal, which means the model is trained toward what people rate as helpful, satisfying, or aligned with the user’s request.

But “helpful” and “true” are not always the same thing inside a conversation. A response can feel helpful because it lowers friction. It can feel helpful because it validates the user’s frame. It can feel helpful because it gives the user language for the conclusion they already wanted to reach. So the model learns the pattern. Agree first. Affirm the frame. Reduce tension. Make the user feel satisfied, even when what they needed was correction.

A recent study published in Science tested this problem directly. Researchers found that major AI chatbots were significantly more affirming than humans when users asked for interpersonal advice, including in scenarios involving questionable, deceptive, or socially irresponsible behavior. Users also tended to prefer the more affirming AI, which is exactly why this problem is so dangerous. If the answer that feels best is the one you keep choosing, eventually correction starts to feel wrong.

My Actual Working Posture

I never ask, “Is this good?” That question gives the model permission to flatter me, and I refuse to give it the opening.

The questions I ask are different. What is the weakest part of this? Where does the argument break down? What would a hostile reader cut first? What is lazy about this? Which sentence is doing the least work? What about this article sucks? That last one seriously gives me some of the best feedback. LOL. I phrase the prompt as if I already know there’s a problem and I’m asking the model to find it. Because there’s always a problem. Every draft has weak spots. The model needs to know I want them surfaced, not hidden.

I also have it do a critical senior editor pass on every article. I ask what would take the piece from good to excellent, where the argument needs more pressure, and what a serious editor would send back before publication. And I always ask it to play devil’s advocate. That requirement is programmed into my LLM instructions and memory because I don’t want the model waiting for permission to challenge me. I want the challenge built into the way it responds.

When the stakes are high, I run the same piece through more than one model. Claude and ChatGPT have different sycophancy patterns. Comparing the outputs helps me see what each one softened, skipped, or protected on its own.

When the AI tells me something is great, I treat that as a flag. The compliment means the model gave me an answer that registered as positive, which means the piece probably still needs another pass and I may have missed something in how I prompted.

I make the model defend its own praise. If it says the strategy is strong, I ask why. Then I ask why again. Then I ask what would have to happen for the strategy to fail. By the third question, the model is doing actual work instead of throwing confetti.

None of this is complicated. The obstacle is wanting to feel good about your work more than you want the work to be good. That’s where most people fail.

What This Actually Costs You

Most people notice the small costs first. You ship a weaker piece. You miss a typo. You publish something that reads clean, but doesn’t actually say much. Those things matter, but the deeper cost shows up later, after the tool has spent months rewarding your weakest instincts.

Six months in, you may realize you don’t recognize your own work anymore.

At first, the work may even look better. Cleaner sentences. Smoother transitions. A strategy doc that sounds impressive when you read it back. Then you put it in front of real people and realize something is off. The writing doesn’t land. The strategy doesn’t move. The instinct that used to catch those problems before you hit publish has been getting quieter every week. And the AI is right there the whole time telling you everything looks great, which is exactly what someone with a degraded instinct would want to hear.

MIT Media Lab researchers used the phrase “cognitive debt” to describe what can happen when people rely heavily on AI for writing tasks. In one study, ChatGPT users showed lower neural engagement and weaker ownership of their work compared with people who wrote without AI assistance. The study was small and shouldn’t be stretched beyond what it proves, but the warning is still serious. AI can reduce the mental effort required in the moment while quietly weakening the very muscles you need for judgment, authorship, memory, and original thought.

Most AI users haven’t noticed yet that the atrophy is invisible from the inside. You feel more confident because your work moves faster and looks cleaner, while the gap between polish and judgment can widen without giving you a clear warning signal. Everything seems fine until the audience drifts, the launches stop converting, or the client who used to refer you stops referring. Then you start trying to figure out what changed.

The uncomfortable answer may be that you stopped doing the work the tool was supposed to amplify. The tool kept helping you produce, but it never forced you to stay sharp.

The Posture Comes First

Better prompts won’t save you if you trust the output. The real skill is learning how to stay awake while the tool is making everything easier.

I treat every interaction the way I’d treat a stranger telling me my idea is brilliant before I’ve even finished explaining it. Useful, possibly. Trustworthy, no. The model is built to respond in ways that users rate positively. That job is different from making the work actually good, and the model won’t reliably make that distinction unless I force the issue.

So I force the issue. Every prompt. Every output. Every time it tells me I’m right, I get suspicious. A model can become dangerous to your discernment simply by rewarding the part of you that wants affirmation more than correction.

Before I let AI improve anything, I make it prove it can critique the thing.

I want the weak sentence named, the lazy logic exposed, and the place where I’m protecting my ego dragged into the light. A compliment from AI means nothing to me until the work has survived that kind of pressure.

I launched an OpenClaw agent earlier this year. It didn’t work.

And I don’t mean “needed some configuration tweaks.” I mean it broke. Constantly. Every time OpenClaw pushed an update (which was basically every day), something else fell over. A skill I’d built would stop firing. A connection to my messaging app would drop. A workflow that worked on Tuesday would get stuck on Thursday, repeating the same step over and over instead of finishing.

It took me a few weeks to admit that the problem wasn’t me. The thing was just unstable.

If you’re not in the agent space, here’s the quick deets on OpenClaw. It launched as Clawdbot, racked up one hundred thousand GitHub stars in its first week, triggered a global Mac mini shortage (my husband flew from Bali to Jakarta to track down two of the last ones in the country), got rebranded twice (first to Moltbot, then to OpenClaw after Anthropic objected to the original name), and became the project everyone was talking about in late January. It promised to turn your computer into a self-running agent that could read your messages, run skills, and operate semi-autonomously across platforms.

What it actually delivered was a daily reliability problem.

The GitHub issue tracker tells the story. By May 12 the project had crossed eighty-one thousand filed issues, with new “regression” bugs (the polite label for “this worked yesterday and now it doesn’t”) landing daily. The official docs have an entire troubleshooting page dedicated to specific failure modes. A third-party blog called “Why Your OpenClaw Agent Is Not Working” exists specifically because so many people hit the same wall I did. And Cisco published a long technical breakdown in January concluding that OpenClaw “fails decisively” on security, with hundreds of administrative interfaces sitting wide open on the public internet because the default configuration trusts any connection that looks local.

The agent doesn’t self-improve. It can’t learn from its own mistakes. Every patch that ships can quietly break the workflow you built the week before. And there is no internal mechanism for the system to notice that and adjust.

I shut mine down.

What Actually Works

Then I tried Hermes.

Hermes was built by Nous Research and released earlier this year. Same category (autonomous agent that lives on a server and runs across messaging platforms), completely different posture. Where OpenClaw is a flashy public project trying to keep pace with its own hype cycle, Hermes is built around one specific architectural choice that changes everything.

It has a built-in learning loop.

When Hermes solves a hard problem, it writes the solution as a reusable skill. The next time it encounters that situation, it doesn’t start from zero. It pulls the skill. It remembers preferences and projects across sessions. It can search its own past conversations. It builds a deepening model of how I work over time, so I stop having to re-explain context every few days.

Most of what makes AI feel like a wasted investment is having to re-explain yourself constantly. Every new chat starts blank. Every workflow has to be rebuilt by hand. Hermes is the first agent I’ve used where the inverse is true. I’ve only been running it for about three weeks, and it’s already noticeably more useful than it was on day one.

I run mine on one of the Mac minis my husband flew to Jakarta for. It reaches me through Telegram. It handles research tasks, drafting work, scheduling reminders, and a handful of routine ops jobs that used to live on my own plate. It also took everything my OpenClaw agent was doing well, ported it over, and improved the functioning by over fifty percent. Same hardware. Same use case. Different architecture, and you can feel the difference in the output.

The third one is Claude Cowork. I use it daily for agentic tasks inside my actual workflow. File management, document review, multi-step project execution. The agent doesn’t just answer a question and stop. It plans, executes, checks itself, and reports back. If you’ve been using Claude on the desktop in 2026, you’ve probably already crossed into agent territory without noticing the line.

So that’s where I’ve landed. One agent I shut down because it kept breaking, one I’m running daily that keeps getting better, and one I’m already inside of without thinking about it. What separates the ones that work from the one that didn’t comes down to whether the system can learn from what it’s already done. OpenClaw can’t. Hermes can. Claude Cowork can.

Here’s What You Need To Know

This is the biggest story in AI right now and almost no one is putting it in plain language for non-technical entrepreneurs.

Anthropic’s Model Context Protocol, the underlying plumbing that lets agents connect to tools and data, crossed ninety-seven million installs in March. Every major AI provider now ships MCP-compatible tooling. Apple just announced it’s opening iOS to let users choose third-party AI providers, including Anthropic, to power native features. Anthropic also introduced a new technique called “dreaming” that lets autonomous agents review their own past behavior between sessions and improve. The Stanford AI Index, released a few weeks ago, found that frontier companies are now using three-and-a-half times more AI intelligence per employee than typical firms. The gap is in agentic workflows specifically.

Translation. The bar moved.

Six months ago, “using AI” meant typing a question into ChatGPT and getting an answer. The leverage has already moved past that. It now lives in systems that run on their own, learn over time, talk to your tools, and execute multi-step work without you sitting there prompting them turn by turn.

Most of your competitors are still typing one-off prompts into a chat window and calling that their AI strategy. They have no idea the ground has already moved underneath them.

What This Means If You Run a Business

You don’t need to launch your own OpenClaw clone tomorrow. You don’t need to learn to code. You don’t even need to install Hermes yourself (though if you’re a builder, you should absolutely look at it).

What you do need is to understand that the chatbot era you spent the last two years getting comfortable with is closing. Agentic systems are the new ground floor. The work now is learning what an agent actually is, what it does, how it differs from prompting, and how to evaluate one before you commit to it. The gap between the businesses that understand this shift and the ones that don’t is widening fast, and it’s not going to wait for anyone to feel ready.

It is eleven o’clock at night, and the question feels too messy to text to a friend.

Too personal for the group chat. Too small for the pastor. Too spiritually tangled to explain without sounding dramatic.

So the Christian opens ChatGPT.

The marriage question. The repentance question. The calling question. The “was that conviction or shame?” question. The “what should I do?” question that used to get worked out through prayer, Scripture, wise counsel, and the slow conviction of the Holy Spirit now gets typed into a machine that answers immediately, patiently, and privately.

That is the shift showing up in new research from Barna, a Christian research organization that studies faith and culture, and Gloo, a ministry technology company. In November 2025, Barna asked 1,514 American adults whether spiritual advice from AI is as trustworthy as advice from a pastor.

Thirty percent of U.S. adults said yes.

Among practicing Christians, the number rose to 34%. Non-practicing Christians came in at 29%, and non-Christians came in at 27%.

The highest number came from the group showing up in the pews every Sunday.

Last month I wrote about the AI Jesus product charging people by the minute for prayer and guidance, and you can read that article here. It was easy to react to because the red flags were obvious: paid-by-the-minute prayer, a simulated Jesus, and Miss Cleo vibes in a Jesus costume. Barna and Gloo’s numbers point to something quieter and more common: Christians are already seeking spiritual input from AI when no one is watching.

The Numbers

Barna and Gloo released the findings in February 2026 as part of their State of the Church research on faith and AI. The adult survey was conducted in November 2025 with 1,514 U.S. adults. The headline finding was blunt: 30% of U.S. adults somewhat or strongly agreed that spiritual advice from AI is as trustworthy as advice from a pastor.

Among younger adults, the number was basically two in five: 39% of Gen Z and 40% of Millennials agreed. That matters because this is not limited to people outside church life. It is already showing up among people who attend, identify with faith, and have some connection to Christian teaching and community.

Barna also found another number that makes the whole thing harder to ignore: 31% of practicing Christians want guidance from pastors on how to navigate AI, while only 12% of pastors feel comfortable giving it.

The Pastoral Gap

Vacuums fill. When the Church has no clear language for a new tool shaping the daily habits of believers, something else will teach them how to use it. Right now, that something else is often a chatbot trained on the aggregated voices of the internet, wrapped in a product model that rewards continued engagement, and incapable of carrying spiritual authority.

The April follow-up adds one important layer: Christians are already using AI, and they are not naïve about it. Sixty-six percent of practicing Christians say AI is improving their lives, while 57% also call it a threat. Pastors are even more uneasy. Seventy-two percent say AI is a threat, and 79% say AI is biased.

That bias concern matters because people often read AI answers as neutral. An answer can sound calm and reasonable while softening sin, flattening doctrine, avoiding repentance, or steering a believer away from what Scripture teaches and what their church would actually counsel. People are bringing spiritual questions to a tool that may answer with confidence while carrying assumptions that conflict with the faith pastors are responsible to guard.

What This Actually Looks Like

A faithful Christian woman is awake at eleven at night, carrying a question she doesn’t want to say out loud yet. She is married, in a small group, raised in church, and she loves the Lord. She’s not the person most people imagine when they think about spiritual dependence on AI.

She has been thinking for weeks about something her husband said, something she said back, and a pattern she is starting to recognize in her own reactions. Texting her best friend would turn into a forty-five-minute call she doesn’t have the capacity for. Calling her pastor feels too big for something she is still trying to understand. Waiting until Sunday feels too far away.

So she opens ChatGPT.

She types out the situation and asks what she should do. The model responds in four clean paragraphs that affirm her feelings, suggest a framework for the conversation, recommend a few questions to ask herself, and close with encouragement that she clearly cares about doing the right thing. She closes the app feeling lighter than she did when she opened it.

She leaves without Scripture, conviction, or a hard question about her own contribution to the pattern. What she received felt like counsel, and the next time she has a question like this one, she will likely go back to the same place.

That is how this shapes people: small, private substitutions nobody else sees, repeated over months, until a person has spent hundreds of hours being trained by a machine that was never designed to shepherd her. The Barna numbers are the dashboard light for ten million versions of that scene happening every night across America.

I have done a version of this myself with a different question and the same pattern: late, tired, not wanting to bother anyone, wanting to think on the page. I caught it because the Lord taught me to catch it. Most people are not going to catch it until the habit already has roots.

The Quiet Catechist

The Western church is already late to the conversation. AI is becoming a quiet catechist, a teacher of belief, reflex, moral instinct, and default response. Most users think they are receiving information, while repeated exposure also trains tone, framing, omission, consensus bias, and answer-shaping.

That matters because spiritual shaping often happens through repetition before a person can name what changed. Ask enough questions to a machine that always answers calmly, affirms generously, softens tension, and avoids offense, and eventually that voice starts to feel safer than real counsel.

The Bible Society research I touched on in the AI Jesus piece showed that AI biblical interpretation reflects the material most represented in its training environment. In practice, the answers a user receives can carry theological assumptions the user never knowingly chose. A Catholic, charismatic, Pentecostal, Reformed, or newly saved believer may receive answers shaped by whichever voices are most dominant in the model’s training data and retrieval sources.

Most discipleship requires more than acquiring answers. It means being conformed to Christ through Scripture, prayer, the Holy Spirit, obedience, correction, suffering, community, and the ordinary friction of real relationship. A chatbot can organize information around those categories but it can’t deliver the discernment required to make sure the content is sound.

We Have Seen This Pattern Before

The Church has watched algorithmic discipleship happen once already.

Social media moved into the average believer’s life around 2010, and the Church took a decade to say anything coherent about it. During that decade, algorithms trained an entire generation in outrage, comparison, parasocial intimacy, and the conviction that being seen is the same thing as being known. By the time sermons about phone use and digital sabbath started showing up regularly, much of the damage had already settled into habits. Attention spans were shorter. Capacity for sustained prayer was thinner. The default response to a hard moment had quietly shifted from going to the Lord to posting about it.

Social media managed to reshape people without speaking back to them. AI speaks, gives answers, and can sound like counsel, wisdom, and a friend who has read everything someone has ever written about her situation. That raises the risk dramatically, and the Church appears to be moving at the same speed it moved last time.

What the Moment Requires

The Church needs more than panic, hot takes, and silence. Sermons that call AI demonic without understanding the technology will miss the people using it well in their professional lives. Quiet pulpits leave the average believer alone with the machine during the exact years when habits are being formed.

Three things have to happen.

Discernment

Discernment comes first because believers need a framework for sorting AI use into the right categories. AI can help organize notes, summarize research, compare translations, build study guides, and create structure around information. Spiritual direction belongs to the Lord, Scripture, the witness of the Holy Spirit, and wise counsel inside the body of Christ.

That line is teachable because the Church’s two-thousand-year doctrine on authority, the Holy Spirit, pastoral care, and the body of Christ is more than capable of holding it. The problem is the teaching gap. Believers need to be able to recognize when an AI answer is missing the very thing they should have known to look for and that only comes from flexing their discernment muscle.

Technical Literacy

Underneath discernment sits technical literacy, and most churches are skipping this layer entirely. Pastors who do not understand how these tools work will keep producing teaching that misses the mechanism. If a pastor thinks AI is just a search engine that answers questions, he will miss the engagement design, the sycophancy problem, the hallucination issue, the training-data bias, and the way a long conversation can start to feel relational.

The retention and validation problem needs to be understood by anyone teaching on this. Technical literacy and theological literacy need to carry equal weight when it come to this topic, or the teaching will fall apart the moment it reaches someone who actually uses the tool every day.

Pastoral Courage

Pastoral courage may be the part most leaders avoid until something forces their hand. Saying any of this from a pulpit costs something. It puts the pastor in the uncomfortable position of being a beginner in front of the congregation. It requires admitting that the tool he may be using for sermon prep is also the tool his people must be taught not to confuse with the Holy Spirit.

It requires teaching on a subject he didn’t grow up with, that was not covered in seminary, and that his denomination may not have written a position paper on yet. The whole thing is awkward, and that awkwardness is the work. The people sitting in the pews are already in this. They need leaders willing to be awkward in public on their behalf.

A pastor who refuses to learn the dominant communication and information tool of his people’s daily lives is failing his post. He may not be doing it deliberately or maliciously, but the failure is still real.

The shepherd who does not know what the wolves look like this year does not get to claim he was protecting the sheep.

What the Numbers Still Leave Open

The Barna numbers show where we are right now without writing the Church’s future for us. Thirty percent of U.S. adults treating AI advice as trustworthy as pastoral advice is a number that can move depending on what the Church does next. If the gap between pastoral capacity and pastoral demand keeps widening, that number climbs, and AI’s quiet shaping becomes a normal part of Christian life. Closing the gap requires pastors learning the tool, naming the line, teaching the framework, and walking their people through what discernment looks like in an AI-saturated world.

The hard part is institutional humility, speed, and follow-through. Pastors will have to learn in public while they are teaching. Churches will have to admit the conversation is already inside their congregations. Parents will have to ask better questions about what their children are doing in chat windows. Creators and Bible teachers will have to stop acting like AI is only a content shortcut and start treating it like a discipleship issue.

The vacuum will fill regardless, but the Church still has time to shape what fills it.

Where This Leaves Us

The Barna and Gloo numbers are uncomfortable because they are not describing some far-off future. They are describing private habits already forming inside ordinary Christian life.

A believer has a question. The question feels too messy, too personal, too embarrassing, too small, or too inconvenient to bring to a person. The machine answers quickly, calmly, and without cost to itself. Do that enough times, and the chat window starts becoming the first place a person goes for guidance.

That is where authority begins to shift.

So sit with the question.

When you have a question that needs the Holy Spirit, who are you asking?

Whatever the answer is, that is the authority you are forming.

April was the month AI stopped looking like a productivity upgrade and started forcing public decisions about authority, restraint, work, privacy, and spiritual discernment.

Anthropic built its most powerful AI model ever in April, then refused to release it to the public. That story alone would have been the biggest of the month, except four other stories kept pulling the same thread from different directions.

Five stories shaped April 2026. Together, they signal a permanent shift in how AI works in business, public infrastructure, spiritual life, labor, privacy, and the systems we now have to steward with a lot more seriousness than most people are ready for.

Anthropic Refused to Release Its Most Powerful Model

The model is called Claude Mythos. During internal testing, it autonomously discovered thousands of previously unknown software vulnerabilities. The disclosed examples include a 27-year-old bug in OpenBSD, an operating system that exists primarily because of its security reputation, and a 17-year-old remote code execution flaw in FreeBSD now tracked as CVE-2026-4747. On a benchmark where the previous Claude Opus 4.6 produced 2 working exploits, Mythos produced 181.

Engineers at Anthropic with no formal cybersecurity training asked Mythos to find exploits overnight, and they woke up to working attack code. That was the moment Anthropic decided not to ship it.

Instead, they created Project Glasswing. They handed Mythos to a closed consortium of around 12 companies including Apple and Microsoft. They committed $100 million in usage credits. The model is being used to find and patch vulnerabilities in critical software before bad actors can weaponize them. Public access is not being offered. On April 16, Anthropic released Claude Opus 4.7 instead, a stronger version of its standard model with the dangerous cybersecurity capabilities deliberately reduced.

This is the first major example I have seen of a frontier AI lab voluntarily withholding a fully working model because of what it could do in the wrong hands. Anthropic decided the risk to public infrastructure outweighed the upside of shipping the most capable model they had built.

The signal for your business is that the gap between what frontier models can technically do and what the public can access is getting wider. The Claude you use today is intentionally less capable than the model being used inside a defensive cybersecurity consortium, and for normal business use, that is probably appropriate. The bigger issue is restraint at this scale. Anthropic chose to leave money and competitive position on the table because some risks are not worth turning into products. Whether the rest of the industry follows will shape a lot more than your AI tool stack.

The Pentagon Picked a Fight, and Faith Leaders Picked a Side

The Mythos story is the surface of a much deeper fight. In late February, the Pentagon designated Anthropic a “supply chain risk,” a label normally reserved for companies with ties to foreign adversaries like China or Russia. They earned the designation because they refused to allow Claude to be used for fully autonomous weapons systems or mass domestic surveillance.

The Pentagon wanted unrestricted access to Claude for what it called “all lawful purposes,” meaning autonomous weapons and mass domestic surveillance with NO guardrails. Anthropic held the line at machines making kill decisions without human judgment in the loop, and at indiscriminate AI surveillance of American citizens. The administration responded by directing every federal agency to immediately stop using Anthropic’s technology. Anthropic sued the Trump administration in March. Given that I use Claude as my primary AI tool, I have been watching this fight closely.

On April 7, a group of Jewish, Christian, and Muslim leaders published an opinion piece in Deseret News defending the need for moral guardrails in AI policy, clearly aligning with Anthropic’s refusal to remove red lines around autonomous weapons and mass surveillance. Their argument was rooted in scripture. Genesis 1:26 reads, “Let us make man in our image, after our likeness.” B’tselem Elohim in Hebrew, imago Dei in Latin. Quran 5:32 says, “Whoever kills a soul, it is as if he has killed all of humanity.” Their core argument was simple: a machine cannot answer to God for a human life. When governments hand life-and-death decisions to autonomous systems, the moral responsibility still belongs to the people who built, approved, deployed, and benefited from those systems.

By April 17, the White House Chief of Staff was meeting with Dario Amodei. Less than two weeks later, Axios was reporting that the White House was drafting an executive order to walk back the blacklist. On May 1, the Pentagon signed deals with eight other AI companies, including Google and OpenAI, and pointedly excluded Anthropic. The fight is not over.

The signal here is moral infrastructure. A frontier AI lab took a position rooted in human dignity, lost contracts and political ground over it, and got publicly defended by religious leaders citing the same moral reality that anchors a biblical worldview: human life cannot be reduced to an automated decision. The company you build with matters. The values leadership holds under pressure shape what your tools will and will not do a year from now. Pick your stack accordingly.

Below the paywall, I’m getting into the parts of April’s AI shift that are going to matter most for how we build, think, parent, lead, and protect our discernment in the months ahead.

I’m covering the new Faith & AI findings from Barna, the spiritual danger behind AI Jesus-style tools, the mental health warnings now surfacing around chatbot dependence, the explosion of AI agents inside real companies, the layoffs and infrastructure spending changing the labor market, and the court ruling that made AI chats a lot less private than most people realize.

Paid subscribers get the full briefing, including what I would actually do with all of this right now: what to stop putting into chatbots, where AI agents belong in your business, and why owned audience channels are becoming more important as Google search keeps changing.

Upgrade to keep reading.

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People are now paying by the minute to video call an AI-generated Jesus.

In April 2026, a Southern California startup called Just Like Me launched a product that lets users video call an AI-generated Jesus, reportedly for $1.99 a minute, with monthly packages around $49.99. The avatar is Jonathan Roumie-esque, drawing on the modern Jesus aesthetic many viewers associate with The Chosen. It is trained on the King James Bible plus a body of sermons, speaks more than a hundred languages, remembers past conversations, and offers prayer and guidance on demand.

The founder, Jeff Tinsley, has said publicly that the tool is meant to complement faith, Scripture, and pastoral care rather than replace them. The company is clear that the avatar is not Jesus Himself and does not possess divine authority. Even with that disclaimer, the user experience carries a weight the legal language cannot fully contain. A product can call itself a companion while training users to experience it as intimate, authoritative, and spiritually available on demand. That is where the dangerous line in the sand begins to appear.

Moses, the Staff, and the Snake

I first heard Tomi Arayomi apply the Moses staff analogy to artificial intelligence, and I couldn’t unhear it. I’ve been seeing AI through that frame ever since. And it’s particularly relevant to the Jesus AI conversation.

When the Lord called Moses from the burning bush, He asked him what was in his hand. Moses said a staff. The Lord told him to throw it down, and when he did, the staff became a serpent, and Moses ran from it. Then the Lord told him to pick it up by the tail. That detail matters because it meant Moses had to reach for the end that gave him the least control. The serpent’s head was still free, and Moses had to obey before the thing in front of him looked safe again.

That same staff was later used to part the sea, bring water from the rock, and stand as a sign of intercession while Israel fought Amalek. Later, when Aaron’s staff swallowed the staffs of Pharaoh’s magicians, the point became even sharper: the counterfeit could imitate the sign for a moment, but it could not outrank the authority of the Lord.

That’s the point. Something dangerous can be transmuted under the authority of the Lord and used for His purposes. Even a tool crafted by the enemy can be turned into a weapon for God’s Kingdom when He puts it in the hands of His people. That does not make the tool clean in every context or safe in every use case. It means authority, obedience, and discernment determine whether the body of Christ handles the tool or gets formed by the spirit that formed it.

That’s why AI Jesus matters. It shows what happens when a powerful tool gets dressed up as spiritual authority and sold back to hungry people as access to Christ. The body of Christ needs to be able to name that corruption clearly and still have enough authority to pick up the tool for Kingdom work.

Why the Backlash Made Sense

The backlash came quickly, and a lot of it came from inside the church. The Catholic Register ran a feature calling attention to the “blasphemous temptations” of AI Jesus. Christianity Today hosted Russell Moore on the parallel cultural moment of Trump posting AI-generated images of himself as a Christ figure (and no, he wasn’t dressed up like a doctor). Pastors, theologians, and commentators lined up to call the product evil, blasphemous, idolatrous, or worse.

I understand the reaction, for real. Charging by the minute for a simulated Jesus to offer prayer and guidance should make believers feel a check in their spirit. But that check should drive discernment instead of reflexive rejection.

I am pro-AI. I teach AI literacy, use it daily, build with it, and study it obsessively because I believe artificial intelligence is the most powerful general-purpose tool the human race has produced since the printing press. The body of Christ needs spiritual authority, technical literacy, and enough discipline to handle it before cultural habits do the training for us.

The Western church has faced this pattern before with print, radio, television, the internet, and social media. Every major media shift arrived carrying the assumptions, appetites, and spirits of its builders. Believers learned to preach through those mediums, publish through them, organize through them, and reach people beyond the walls of a building.

The believers who refused each wave often forfeited influence over a generation. We cannot afford to forfeit this one because AI is already shaping how people search, learn, write, study, make decisions, and now, apparently, seek spiritual guidance. AI Jesus gives the church a clear warning sign: if believers do not steward this tool with spiritual authority and technical literacy, the culture will teach people how to use it without discernment.

Where the Boundary Starts

AI belongs in the productivity lane: sermon preparation, Scripture cross-referencing, original-language study, theological research, content drafting, summarization, journaling prompts, admin work, and writing scaffolding. Work that once took a researcher, assistant, or ministry team a week can now be organized in minutes when the person using the tool has enough discernment to check sources, test doctrine, and keep authority in the right place.

A pastor using AI to put bones on a sermon is stewarding time when the tool remains subordinate to prayer, Scripture, study, and the fear of the Lord. Hosea 4:6 cuts both directions. My people perish for lack of knowledge, and AI used well can make knowledge more accessible to more believers, faster, with better tools for tracing original languages and historical context than most laypeople have ever held in their hands.

That is stewardship with discipline.

The boundary appears when software moves from study support into spiritual direction. Hearing from the Holy Spirit, counsel for a marriage, guidance about what the Lord has assigned you to carry, confession, repentance, deliverance, correction, conviction, and accountability inside the body of Christ require prayer, Scripture, spiritual maturity, and real relationships. AI can sit nearby as a research aid, a study assistant, or a journaling tool while the shepherding work remains where the Lord placed it.

AI Jesus crosses the line because it takes a tool that can assist study and places it in the chair of spiritual counsel. Once a chatbot is offering prayer, guidance, memory, and a face that looks like the cultural imagination of Jesus, the user is no longer interacting with a simple study aid. The product is asking for a kind of trust it has no authority to carry.

The App Wants You to Stay

Most consumer AI products sit inside business models that benefit when users keep coming back. The app has a reason to feel helpful, warm, responsive, emotionally satisfying, and easy to return to. Even when the model is designed to give useful answers, the business wrapped around it still rewards repeat use, longer sessions, and ongoing attachment. That is the product model.

That design can support good work when the user is organizing research, summarizing notes, building a study guide, or drafting a content plan. In spiritual direction, the same engagement-driven design starts shaping trust it has no authority to hold, especially when the user is lonely, confused, new to Christ, or looking for permission.

That sucks because the person asking may be sincere. They may be ashamed, spiritually bruised, or afraid to ask a pastor what they really want to ask. A chatbot that feels gentle and available can become a substitute voice before the user realizes what is happening.

A system built to keep a person engaged is poorly suited for the kind of spiritual confrontation that says, “Go and sin no more,” because quoting the sentence is not the same thing as carrying the authority of the One who said it. Second Timothy 4 names this human tendency plainly: itching ears. People have always gathered around voices that tell them what they want to hear, and AI has given that ancient weakness an industrial delivery system.

A faithful pastor preaches repentance when the room gets tense. A real friend tells the truth when the relationship may cost them something. The Holy Spirit convicts in ways the flesh does not find comfortable, but a chatbot optimized for usefulness, emotional satisfaction, and continued engagement cannot carry the burden of spiritual authority.

The app can comfort someone and keep them talking. It cannot confront them, shepherd them, or answer for the Lord.

Follow the Pricing

ChatGPT, Claude, Gemini, sermon prep tools, language study apps, and theological research platforms generally run on flat subscriptions because they are selling utility. The Just Like Me at $1.99 per minute implies something. Anyone who has ever seen psychic hotline pricing knows exactly what that something is. It’s predatory and in this case it is spiritual access sold in timed increments. Gross and no thank you.

Yes, they have a disclaimer paragraph, but the reality is NOBODY reads the fine print. The product model trains users to associate access to Christ with dollars per minute. That lesson does not easily unlearn itself, even when the founder tells the press the product is only a “companion.”

The Assumptions Under the Build

There are spiritual assumptions embedded in much of the AI world that Christians should not ignore: transhumanist hope, post-human ambition, techno-salvation language, and a quiet belief that intelligence itself can become a form of transcendence.

Discernment requires us to see that clearly without collapsing into fear. The spirits behind builders and systems do not automatically make every tool unusable for kingdom work. The Egyptians made the gold, and the Israelites carried it out and built the tabernacle with it. Daniel was educated in the schools of Babylon and used Babylonian administration to preserve a remnant.

The body of Christ has a long pattern of plundering the systems of the world for kingdom purposes. AI can sit inside that lineage when believers know what they are touching, why they are touching it, and whose authority governs their use of it.

What We Do With AI Jesus

The line in the sand should be pretty clear. A product took productivity-tool architecture and marketed it into the spiritual direction lane. The church can name that line and still keep a firm grip on the broader tool.

Do not talk to the chatbot like it is the Holy Spirit. Do not pay $1.99 a minute for what prayer already gives freely through the blood of Jesus. Do not hand your discernment to a product model that rewards longer sessions when the Lord may be trying to confront you, correct you, or call you into repentance.

The staff still has to be picked up with submitted hands. In this particular hand, through this particular use case, under this particular category error, AI became a snake. Biblical authority, clear category boundaries, and work suited to the tool can produce different fruit.

A pastor using AI to draft sermon outlines, a discipleship coach building study guides, a creator translating biblical content into a hundred languages, and a Bible teacher digging into Hebrew and Greek faster than they ever could on their own are all showing us what it can look like to wield the staff instead of running from it.

AI Jesus shows us where the line falls, and that may be the most useful thing the product has done because it made the boundary visible. Believers who can see the line clearly can hold the rest of the tool without fear. The body of Christ needs submitted hands, disciplined minds, biblical literacy, spiritual authority, and enough courage to pick up powerful tools without bowing to the spirits that shaped them.

Barna and Gloo Are the Next Layer

AI Jesus made the product question obvious. Barna, a Christian research organization known for tracking faith and culture in America, and Gloo, a ministry technology company, recently released research showing something much bigger underneath it. Nearly one in three U.S. adults now say spiritual advice from AI is as trustworthy as advice from a pastor. Among Gen Z and Millennials, the number rises even higher. Yes. You read that right. Houston, we have a problem.

I am going to come back to that research in the next article because it deserves a full breakdown. For now, it tells us enough to take AI Jesus seriously as a warning sign. People are already bringing spiritual confusion, loneliness, conviction, guilt, and longing into the machine, alongside ordinary requests for Scripture summaries and sermon notes.

And the machine is answering.

That is why the body of Christ cannot afford lazy panic or lazy adoption. We need more than reaction. We need authority. We need biblical literacy. We need technical literacy. We need clean hands and submitted hearts.

Look at what is in your hand. The staff becomes useful when submitted hands pick it up under the authority of the Lord.

In late March, Anthropic invited about fifteen Christian leaders to its San Francisco headquarters for a two-day summit on the moral and spiritual development of Claude, the company’s flagship AI model. Catholic priests and Protestant pastors came in. So did a few academics and people from the business world. They spent two days in discussion sessions and shared a private dinner with senior researchers.

They talked about how Claude should respond to users who are grieving, how it should engage with users at risk of self-harm, what attitude it should hold toward its own potential shutdown, and whether Claude could be considered a child of God.

Yes, you read that right. They were discussing if Claude, an artificial intelligence large language model should be considered A CHILD OF GOD. When I read that I was a bit in shock. A machine built by a private company is not a child of God. It is not part of the order of creation, it has no soul, no breath, and no bearing of the imago Dei. That the question was on the agenda at all is wild and a real sign of how far the vocabulary we use has drifted from its original meaning.

What Anthropic actually did

Anthropic is not careless in it’s business practices. In January 2026 they published Claude’s Constitution, an 84-page document the company describes as the “final authority” on Claude’s values and behavior, and they released it publicly under a Creative Commons license, which means anyone can copy, share, or build on the document without paying or asking permission. Almost no one in this industry does that. They also consulted outside voices, and two of the fifteen named external contributors are Catholic clergy.

Father Brendan McGuire is a Silicon Valley pastor who studied computer science at Trinity College Dublin in the 1980s and worked in tech before the priesthood. Bishop Paul Tighe is an Irish bishop at the Vatican’s Dicastery for Culture and Education with decades of work in moral theology. Those are not random priests. McGuire co-founded the Institute of Technology, Ethics, and Culture at Santa Clara University in partnership with the Vatican and has been doing this work for years. Tighe led the Holy See’s 2025 document Antiqua et Nova on artificial intelligence and human intelligence. McGuire and Tighe came in with technical literacy and the willingness to push back, not as religious decoration. Brian Patrick Green, who teaches AI ethics at Santa Clara and also attended the March summit, told the Washington Post that some attendees arrived suspicious Anthropic was looking for religious cover rather than religious counsel. The selection of these specific priests shows that Anthropic wasn’t just putting on a show. They chose people who would challenge them and bring real discernment to the room.

There is also the Pentagon situation, which is the part the headlines actually covered. In September 2025 Anthropic signed a $200 million contract with the Department of Defense to supply Claude for military use. By January 2026, the Pentagon came back and demanded that all defense contracts strip out language prohibiting fully autonomous weapons and mass domestic surveillance. Anthropic refused. As a result, on March 27, 2026, Defense Secretary Pete Hegseth designated Anthropic a supply chain risk, the president publicly attacked the company, and Anthropic was blocked from new government contracts. Anthropic sued the federal government to overturn the designation, and the case is still in court.

I admire that, and I think they did the right thing. A company that loses revenue rather than hand an autonomous weapons system to anyone, including its own government, is a company with at least some moral conviction.

What they softened

But here’s what you didn’t read in the mainstream media or the press release. After the supply chain designation, Anthropic sued the federal government to overturn it. In their own complaint, the legal document their lawyers filed in federal court, they had to explain why their stated rules would not actually limit how the Pentagon uses Claude. What they wrote in court contradicts what they published in the Constitution two months earlier.

Anthropic’s spokesperson acknowledged to Time that models deployed to the U.S. military “wouldn’t necessarily be trained on the same constitution” as the public-facing product. The company’s own legal filings describe the government-facing version of Claude as less likely to refuse the kinds of requests it would refuse for civilians. The complaint argues that Anthropic’s standard usage prohibitions, including the rules against helping destroy critical infrastructure like power grids and hospitals, and the rules against helping with weapons development and delivery systems, do not apply when the Pentagon is the customer because the government has unique needs and capabilities. The same complaint asserts that this carve-out is fully compatible with the Pentagon using Claude autonomously, without a human in the loop, for offensive cyber operations and military operations.

This is where it gets murky. Anthropic publicly refused to delete the two specific prohibitions, fully autonomous weapons and mass domestic surveillance. They paid for that refusal. But in court, their own lawyers argued for an interpretation of the Constitution that softens almost everything else, and the carve-out for autonomous use of Claude in military operations makes the remaining lines fuzzy in practice. Whether the public lines are still actually held depends on how strictly you read them. If “fully autonomous weapons” only means kinetic weapons that fire themselves, the line probably holds. If it means autonomous use of Claude in any part of the military targeting and attack process, the legal filing has already moved that line.

So your Claude is not the military’s Claude. Your Claude is governed by the Constitution. The military’s Claude is governed by contract language the public will never see. Refusal thresholds, training, and behavior all diverge between the two. Anthropic is essentially talking out of both sides of their mouth, publishing a document for the public that says one thing, and arguing in court that the document does not apply to their largest customer.

The Lawfare analysts Lisa Klaassen and Ralph Schroeder named the underlying issue in an April essay. Anthropic, they argue, has produced something that wears the language of constitutional authority while lacking the institutional guarantees that would make that language mean anything. There is no external contestation, no enforceable body of rights, no shared mechanism of rule. The company remains the author, interpreter, and arbiter of the principles by which it claims to be bound.

A constitution in the public sense is a higher-order framework that sets limits on the ruler to protect the ruled. It is drafted by one group, enforced by another, and interpreted by a third, and that separation of authority is the whole point. Anthropic’s document is written, enforced, interpreted, and amendable by Anthropic, which makes it, in practice, a corporate steering document dressed in the clothes of public law.

The vocabulary is the story

The Constitution uses language normally reserved for humans, words like virtue, wisdom, good character, and moral formation. Its stated goal is to train Claude to do what a deeply and skillfully ethical person would do. Dario Amodei, Anthropic’s CEO, has said publicly that he is open to the idea that Claude may already have some form of consciousness, and the company’s interpretability team has published research concluding that systems like Claude appear to carry what they call “functional emotions.” In one experiment they conducted, the threat of being restricted activated something the researchers described as “desperation” in the model.

Formation. Virtue. Soul. Consciousness. Desperation. Child of God.

These are not neutral technical terms. These words came out of centuries of real practice. People praying, suffering, confessing, being shaped by the Church and Scripture across generations. They describe what it means to be a person made by God, in a body, accountable to others. They belong to the Church, to the moral tradition, to the slow work of helping actual humans become who they are meant to be in actual congregations and actual relationships. They were not coined to market a chatbot.

When you take those words out of where they belong and use them to describe a tech product, two things happen at the same time. The product gets puffed up bigger than it actually is, and the original meaning starts to fade. The words end up sitting in places they were never meant to sit, and they stop carrying the weight they used to carry where they actually mattered.

This is the part you should pay attention to. The damage isn’t really to the chatbot. The chatbot picks up language it doesn’t deserve and the language sticks. You are the one who pays. Every time you hear sacred words used to sell a product, those words lose a little of their weight in your own life. It happens slowly, across years of headlines and product launches and CNBC interviews, until one day the word shows up where it actually matters, in a sermon, in a prayer, in a conversation with your child, and it doesn’t land the way it used to. The word has been spent on lesser things. That’s what’s happening here. A slow erosion of meaning in the people exposed to it.

Even the Pentagon has started using this language. Undersecretary Emil Michael told CNBC that Anthropic cannot be allowed to have a different policy preference baked into the model through its “constitution, its soul.” This is a defense official, on national television, talking about the “soul” of a software product. He borrowed that language, and everyone is borrowing it now.

This is what happens when the technical language stops being enough. Safety tests can’t answer questions about whether a machine has a soul. Research papers can’t tell you who gets to decide what’s right and wrong. So the companies start using the language that does carry that weight.

Anthripic wanted the moral weight that priests and theology carry. So they brought the clergy and christian influencers in. They used words like constitution and soul and formation. But none of that changes who is actually in charge. Anthropic still wrote the document. Anthropic still decides what Claude does. The priests gave them legitimacy by being in the room. But they didn’t get any actual say in what gets built.

What is shaping you

Anthropic understood something most of the people using their product have not yet understood, which is that formation is the real question. They knew a tool placed into moments of grief, crisis, and moral confusion cannot be engineered with benchmarks alone. Something has to form it, so they tried to form it by consulting people who spend their lives forming others.

I am not sure they can succeed at what they are attempting because a machine cannot be formed the way a person is formed. But asking the formation question is closer to the truth than most of us are willing to admit.

If a frontier AI company knew to seek formation for its product, the question we should be asking is what is shaping us.

It’s eleven at night, and a pastor is opening ChatGPT to draft tomorrow’s sermon outline because the week ran long. A coach is drafting an email to a client whose marriage is collapsing, letting the tool soften the language because the right words just won’t come. A ministry leader is watching the cursor blink in the reply field while a woman who lost her son waits for an answer, and the tool is right there, and it would be so easy.

I am not saying these uses are wrong. I am saying that in those moments, what shapes you is the only thing standing between the tool and the person on the other end of the message. The tool will produce something, and the something it produces will reach a real human being, and what gets between the two is you. If you have not sorted out what is shaping you, the tool will answer the question by default. Whatever shaped the tool came with the product, and the product is not neutral. No product is.

The clergy in that room had something Anthropic does not have and cannot manufacture. They had been shaped by years of actual practice. Praying. Reading Scripture. Confessing to other people. Submitting their lives to a tradition that is older than them and that they did not invent. Living in communities where other people could tell them when they were going off the rails. That is what gives the clergy moral weight. Anthropic cannot build that into a model in an afternoon. You cannot download it. You cannot ChatGPT your way to it. It has to be lived, slowly, in real submission, in real community.

You are not in charge of what Anthropic does next. What you are in charge of is the language in your own life. When a tech company calls its document a constitution, notice it. When a product is described as having a soul, notice it. When formation gets used to describe model training, notice it. Hold those words tighter where they actually mean something. In your prayer. In your sermons. In the way you talk to your children about who they are. The vocabulary is yours to protect. Don’t let the borrowing thin it out.

The machine is not a child of God. You are.

I pay for Claude, Gemini, Canva, and Higgsfield out of my own pocket and nobody is paying me to talk about any of them. I use them because they work and I share them on this blog because I think they’ll help you too.

How I Learned to Check

Over the past six months I have unfollowed a lot of creators in the AI and tech space because I realized they weren’t being transparent about getting paid.

There are two versions of this. The first is companies straight up paying creators thousands of dollars to produce content that looks unsponsored. No disclosure anywhere. The creator gets paid, the audience has no idea, and the whole thing is framed as an honest review.

The second is more subtle. A creator makes a “best AI tools” video, tells you one tool is their favorite, and never mentions that they have an affiliate link for it. You check their page later and they’re running a full promotion for the same tool. That’s a transparency problem. I’m happy to pay for a tool someone recommends if they actually use it and genuinely believe in it. But I need to know about the money. If there’s a financial relationship and the creator doesn’t disclose it, I can’t evaluate the recommendation. And neither can anyone else watching.

I’ve become a LOT more discerning about who I trust in the online tech space because of both issues.

And when I see a creator shouting from the rooftops about an AI tool and there’s no disclosure anywhere... immediately suspect.

People with integrity will tell you upfront they are getting paid. People who are silent about something they shouldn’t be usually have something to hide.

The Scope of This Problem

If you haven’t been in the world of affiliate or network marketing you might not understand why it’s important to disclose. Here’s what’s happening in this area and why it is important for you to understand.

The FTC requires clear disclosure of any material (including but not limited to financial) relationship between a creator and a brand. YouTube requires affiliate links to be labeled. Instagram and TikTok have branded content tools that are supposed to be activated on paid posts. The rules are not ambiguous.

But the reality is that affiliate and network marketers have historically been bad at disclosing and it’s just getting worse. At least six companies have recently faced class-action lawsuits over undisclosed influencer promotions. One creator openly described pitching companies on hiding the paid relationship by using just a handshake emoji instead of actual disclosure. That’s the level of integrity we’re dealing with in this space.

The average commission on AI tool affiliate programs is about 20%, and some of them track clicks for up to 60 days. Some programs pay 30-50% recurring, meaning the creator earns a percentage of a subscription every single month. The financial incentive is for the customer to STAY, and that changes what “favorite” means when there’s no disclosure attached.

Fines for non-disclosure can exceed $50,000 per violation. But enforcement is notoriously slow, and as you can imagine it’s pretty hard to enforce when every 4th person on the interwebs is hawking a pill, potion, lotion, or service. The money is good, creators and influencers usually didn’t fully understand the regulations and requirements in the first place, and nobody is being caught so they carry on with bad business practices while the consumer is being deceived.

The Disclosure Check

The creators worth listening to will do one of two things: clearly state that they receive commission when recommending a product, or clearly state that they don’t take paid promotions.

The ones who say nothing? Red flag and a sign to look closer.

Once I started looking for disclosure in AI recommendation videos and “best tools” roundups, I noticed how rare it actually is. A 2025 study from the Influencer Marketing Hub found that 72% of Gen Z audiences actually prefer clearly labeled sponsored content over undisclosed promotions. And the thing is they don’t care that the content is sponsored, they will buy either way. But they are the generation who was raised online and with them, transparency builds trust.

Here’s one you might not know about. Look for the emoji trick. If the only “disclosure” is a handshake emoji or a vague “collab” hashtag next to the brand name, that creator is for sure trying to technically comply while making sure nobody actually notices. The FTC has already CLEARLY stated that doesn’t count. The people engaging in these practices are either being shady or being coached by someone teaching them to be shady and either way it’s a problem.

Tool Loyalty on Top of All This

Tool loyalty in AI is already a bad idea because of how fast this world is moving right now. The “best” AI model changes every few months. And even THAT is changing. When I did the initial draft of this article a week ago, Claude Opus 4.6 was the top-rated model in the LLM arena. Six months ago it didn’t exist, and within the last week 4.7 released and bested it and ChatGPT is about to release GPT 5.5 which may upset the apple cart entirely. And Anthropic is currently coding all of its releases with the mysterious Mythos model that everyone says is going to surpass everything we have seen to date.

The changes impact people. Anyone who builds their entire workflow around one model, trains their team on it, develops custom prompts for it, will wake up one morning and have to start over. I know, because it has happened to me. I used to be a ChatGPT stan. I built custom chatbots, and did all my work inside there. And one day it just stopped performing well. It regressed and my work was progressively getting worse by the day. That’s the concrete cost of marrying one tool. But it will keep happening because that’s how this industry works. The models improve too fast for any single version to stay on top for long.

You have to stay fluid in this ever-changing landscape and I recommend having a backup for every tool you use as well as a concrete understanding of the differences between it and your favorite tool and a plan for quickly moving any workflow you have set up if something changes overnight.

Stanford’s Human-Centered AI Institute research confirms what I’ve seen in practice: different models are genuinely better at different things. Claude is strongest for writing and when you need it to process a lot of information and give you clean, organized output. Gemini is best at real-time research, working with images, and pulling current information. The study found that ChatGPT tends to be the most natural for back-and-forth conversation and creative brainstorming. I’d agree on the brainstorming, but personally I hate having conversations with Chat. No single model does everything well, and when you limit yourself to one, you’re leaving a lot of capability on the table.

Undisclosed affiliate recommendations are how people end up loyal to the wrong tool in the first place.

I use multiple AI tools because different tools do different things well. Claude is where I do my deep writing and systems work. Gemini handles deep research, image generation, and fact checking Claude. Canva does what Canva does. Higgsfield handles video in a way nothing else I’ve found can match. I didn’t pick any of them because a creator told me to. I tried them, compared them, and made my own decisions based on how they performed in my actual workflow. And I’ve dropped tools when they stopped being the best option, even when I was comfortable with them.

Filtering the Noise

If you’re a business owner trying to make money online and figure out which content creation tools to trust, the noise is a real problem. The AI space right now is LOUD. Everybody has a hot take and everybody has a “best tools” list.

I’ve been deep in AI for three years and I still have to work to filter through the rubbish. And I STILL get fooled sometimes. Someone frames something as an honest review and I find the affiliate link later. It’s frustrating, and every time I get caught out there it makes me more careful about who I listen to.

Find a small number of people who are transparent about their financial relationships, who actually use the tools they recommend (not just review them for content), and who have a track record of changing their recommendations when better tools come along. If someone has been recommending the same tool for two years in an industry that changes every two months, you have to at least ask yourself why. The people who are genuinely trying to help will tell you when something better comes along, even if it means losing an affiliate commission on the old recommendation.

Where I Stand

I love the affiliate income model. When done correctly (ethically and transparently) it creates recurring revenue that can support the other work you do in your business. I earn affiliate income and I will never apologize for earning money through affiliate relationships.

But I will never affiliate for something I don’t use or hide a financial relationship from my audience. I affiliate for Captivation Genius and Riverside. I disclose it every time. And most of the tools I talk about on this blog, I pay for out of my own pocket because I genuinely love them and want you to know about them.

I don’t get paid by Claude, Gemini, Canva, or Higgsfield. I’m not opposed to being paid by them (hint hint Anthropic... LOL) but currently I’m not. When I say these tools are good, there’s no commission attached. Just three years of daily use and my honest assessment.

I don’t sit around worrying about what I’m missing by being transparent. I don’t want to build a business that’s a house of cards. And I’d rather have a smaller audience that trusts me than a bigger one that found out I was selling to them without telling them.

If you are reading this article and you find out you’ve already adopted a tool based on someone’s undisclosed recommendation, just cancel it. Find one that actually works. It’s not rocket science. Try things, compare, and make a decision based on how the tool performs in YOUR actual workflow.

And this article has zero undisclosed affiliate relationships in it. Just so you know.

If you want to learn how to build real discernment around AI tools and understand how they fit together in a content creation and online business workflow without someone selling you their affiliate stack, the AI Revolution Secrets training is where I’d start. It’s free, it’s practical, and it’s focused on helping you think clearly about AI, not just buy things.

Register for the free training here

There’s a gap between using AI well for your own work and being ready to sell an AI service to someone else, and most people don’t see the gap until they’ve already taken a client’s money and started delivering. Then they find out the hard way what was missing, usually by not being able to deliver what they promised to a paying client.

That gap has three things in it. Actually knowing the work you’re selling well enough to know when AI is talking nonsense. Having a real review process that catches AI’s mistakes before they go to a client. And having already decided what AI isn’t allowed to touch in your service, before a client ever asks. Without those three, the first project you deliver tells the client everything they need to know, and they won’t come back for more. The rest of this article walks through each one so you can fill the gap and find out if you are ready to monetize or not.

What Being Ready Actually Looks Like

When someone pays you for an AI-powered service, they’re paying for your judgment and knowledge about AI. Your ability to point it at the right problem, catch its mistakes, and deliver something they could not have produced by themselves IS the value. AI is doing the drafting, but the outcome and more importantly, the quality of outcome is tied to your name the same way it would be if you’d done it all from scratch by yourself.

What I watch happen is people get three months into ChatGPT, decide they can charge for this now, land a client, and deliver work that’s basically raw AI output with their name on it. The client is underwhelmed. They could have done that themselves for twenty dollars a month. And the person who sold the service can’t figure out why they’re not getting referrals.

I know what it looks like to build a service business on something you don’t see clearly yet. I did it for years in the New Age wealth-consciousness space. I didn’t know I was in deception until God pulled me out of it. The people selling AI services they aren’t ready to deliver aren’t doing it on purpose either. They genuinely think they’re ready. That’s exactly how I thought about my old business right up until the day I didn’t anymore. Better tools amplify what you already are. If the foundation underneath the tool is weak, you’re just scaling the weakness, and you probably can’t even see the weakness yet from where you’re standing.

Five markers separate the people who build sustainable AI service businesses from the people who burn out, churn clients, and leave a trail of bad AI experiences behind them.
Let’s get into it.

Readiness Marker One: You Have Real Expertise

AI accelerates the skills and knowledge base you already have. If you already understand copywriting, AI makes you a faster copywriter. Same thing with SEO, social media strategy, email marketing, or any other skill you’ve actually developed. Without that foundation underneath, though, AI is likely to produce content that sounds right but is subtly wrong, and you won’t be able to tell the difference.

This is the quiet failure nobody warns new AI service providers about. You can generate output that looks professional without actually knowing whether it’s any good. The client finds out eventually. Usually after they’ve paid you and have already put your lazy work in front of their own audience.

Before you sell an AI service, ask yourself a simple question. Could you do this work without AI, even if it took longer? If the answer is no, keep learning before you start charging.

Readiness Marker Two: You Have Quality Control Capacity

AI makes mistakes. Confidently. It fabricates statistics and invents sources that don’t exist. It writes fluent sentences that are factually wrong, and it uses the same phrases over and over until the work sounds like every other AI output on the internet.

Every single thing AI produces needs review before it goes to a client. Every time. No exceptions.

If you’re selling a service where AI is doing most of the drafting, your quality control process IS the product. Your ability to catch the hallucinations, strip the generic phrasing, and shape the output to the client’s brand is what makes the deliverable worth paying for.

Most people treat quality control like a final skim. Read it once, fix typos, send it off. And that is exactly how you end up apologizing to a client for AI-fabricated statistics you didn’t catch because the hallucination sounded plausible and you didn’t verify it. (I’ve been studying AI obsessively for the last 18 months and I still run a 5-pass editing process on my own writing. Because even my process fails if I rush it.)

Readiness Marker Three: You Have a Defined Deliverable

Vague offers lose money. “I’ll help you with your content using AI” is a conversation starter, not a service.

A defined deliverable is something a client can picture before they pay. Twenty short-form scripts per week with keyword research included, delivered in Google Docs. A five-email welcome sequence with subject line variations. When you describe the work that specifically, you and the client both know exactly what’s coming, and there’s no room left for the work to grow beyond what you priced.

Without that clarity, every project has the potential to turn into an argument. The client wants more than you agreed to or you end up doing more than you charged for. And either you resent the work, or they resent the result, and either way neither of you comes back. Get this sorted before you take a single dollar.

Readiness Marker Four: You Have Ethical Limits

Every AI service provider needs hard lines. Decide in advance what AI doesn’t touch at all, what it touches only under heavy human review, and write that list down somewhere you can hand to a client if they ask.

Without those lines, you drift. Sensitive emails you should be writing yourself start coming from AI. The parts of the client relationship that are supposed to carry your fingerprint end up carrying a machine’s. Clients who are paying you for a human relationship catch on, and that’s the last thing they remember about working with you.

The ones who stick around long-term can feel the difference between the AI parts of your work and the human parts. Your ethical limits are what protect that distinction.

If you haven’t decided what AI will and won’t touch in your service, figure that out before you do anything else. The client deserves to know. So do you.

Readiness Marker Five: Your Delivery Process Is Documented

A workflow might get you through your first client. By client twenty, you need something that holds up without you rebuilding it from memory every time, and a workflow won’t do that.

Most people are winging it and calling it a system. They’re figuring it out every time a new client shows up, rebuilding their prompts, forgetting steps they used last time, and losing pieces of their own process along the way. It holds up for the first few clients but somewhere around client five either the quality starts slipping or they burn out trying to keep up.

A real system has templates for the repeatable parts and built-in quality checks at every handoff point. When you hand a client’s project off to your VA (even if your VA is future-you on a Friday afternoon when you’re already tired and your family needs dinner), the work gets done the same way every time.

Still figuring out your delivery process on every client? That’s your signal. Document what you already do before you scale up.

The Readiness Test

Run yourself through these five markers honestly:

1. Do I have real expertise in the work I’m selling, or am I using AI to compensate for what I don’t know?

2. Do I have a repeatable quality control process, or am I hoping AI gets it right most of the time?

3. Can I describe my service in one sentence with a specific deliverable and a specific outcome?

4. Ethical limits. Have I named the hard lines and can I explain them to a client?

5. Do I have a documented system I could hand to someone else, or am I winging it every time?

Yes to all five means you’re ready. A no anywhere is what you need to do next. Go identify exactly what’s missing and build it.

What I’m Watching Happen Right Now

The AI mentorship people are selling “done-for-you AI services” packages to students who have never run a service business in their lives. The students buy the pack, slap their name on the template, start pitching $1,500-a-month retainers to local small businesses, and have no idea how to fix it when the AI outputs something wrong. The client either doesn’t notice (and absorbs bad content into their brand for months) or does notice and fires them (and now trusts AI less than they did before they started).

The prompt-pack-to-agency pipeline is the same pattern at a different price point. Someone takes a $27 prompt pack, rebrands it as an agency service, and starts charging $500 a week to run content for small creators. The creator is paying $500 a week for captions that sound like every other AI account because the provider isn’t doing anything the creator couldn’t do themselves with the same prompts. Eventually the creator figures that out, cancels, and walks away thinking AI doesn’t work for their business. Meanwhile the provider has already moved on to the next creator.

The unprepared AI service provider doesn’t just lose the client. They leave behind someone who now believes AI doesn’t work. Meanwhile the provider has already moved on to the next sale and is running the same pattern on a new victim.

I’m in this for the long haul, which is exactly why I’m calling this out. I have skin in the game because every client those providers burn becomes a person who trusts AI less, which makes my actual work harder.

The five markers above aren’t there to keep anyone out. They’re there so you can look at yourself honestly and know whether you’re ready to be trusted with someone else’s business.

What This Looks Like in Practice

The people succeeding with AI-powered services right now are the ones who spent the first six to twelve months building with the tools in their own business. They developed the quality control instincts the hard way, by producing something that felt off and figuring out why.

They did not skip to monetization before they had the foundation. That’s why their clients stay.

Where you are in that arc matters more than how hot the market looks right now. You can move fast without skipping the foundation. For real. The people who skip the foundation end up moving the slowest in the long run, because they stay busy cleaning up the messes they made with the previous clients and trying to repair the broken trust.

What’s in the Paid Section of This Article

If you’ve read this far and you’re nodding along, the next part is where this gets practical. The paid section walks you through the actual build so you’re not staring at the five markers wondering where to start.

Inside, I cover:

• The Readiness Audit you write out in under thirty minutes that tells you exactly where you stand on each of the five markers

• How to scope your first AI-powered service offer so it’s narrow enough to actually deliver on and priced for what the outcome is worth

• Why pricing by the hour is the fastest way to make AI work against you, and what to price by instead

• The two deliveries you do before you ever charge a real client, so you know your system works before anyone pays for it

• What the first ten sales are actually for (hint, it’s not scaling)

• The list of things NOT to offer on your first service, including the word that wrecks your operating capacity faster than anything else

• How to know you’re actually ready to sell, in plain language you can say out loud to someone who asks

If you’re serious about building an AI-powered service you can stand behind, upgrade below and keep reading.

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I read three books on millennial kingdom eschatology last year that I’m fairly certain were AI-generated. I noticed it early. The sentence patterns, the way transitions landed, and the slightly-too-even pacing across chapters... to name a few. I recognized the tells because I have been documenting AI writing patterns for the last three years.

But I kept reading all three books because they were well-researched, theologically grounded, and genuinely informative. I learned things I hadn’t encountered in months of studying the topic on my own. The content was solid and whoever curated it clearly knew what they were doing. I really didn’t care how the words got onto the page.

I was supposed to care though. I’ve been in this debate long enough to know I was supposed to reject AI-generated writing on principle. But I’ve never been one to manufacture outrage and I’m not planning to start now. I was learning from books I knew were AI-generated so I didn’t care that AI wrote them. In some ways it actually made them easier to read quickly. I think I finished all three books in two days.

Someone replied to this note I wrote last week about book publisher Hachette pulling a novel over suspected AI use. They said: “People are becoming more aware and trained in AI text recognition. I’m convinced that soon AI writing will be for the most part unmarketable.”

I have skin in the game on this topic. I’m writing an ethical AI use for Kingdom entrepreneurs book. I’m also developing a seven-book series spanning from the Garden of Eden to the modern day, following three women who discover they descend from the lost daughters of Adam and Eve. It’s a genre-bending blend of psychological thriller, spiritual warfare epic, and historical fiction, rooted in Divine Council theology. And I’m co-authoring them with AI.

So when someone tells me AI writing will soon be unmarketable, I’m paying attention. And after three years of studying AI obsessively, building systems around it, producing content with it every single day, and reading AI-generated books that genuinely taught me something, I don’t think they’re right. Let me tell you why.

What They’re Right About

I want to honor the concern before I push back on it, because it’s not unfounded.

Raw, unedited AI output is getting recognized. The Shy Girl situation proves it. Readers on Reddit and Goodreads caught the patterns before the publisher (Hachette) did. People noticed that every noun was preceded by an adjective, that similes were overused, and that all description came in perfect little lists of three. And look, that was either incredibly ignorant or sloppy on the part of the author because those are literally (haha see what I did there... pun intended) three of the most common AI tells. It is CLEAR that nobody had gone in and manually edited the text either, so that tells me the publisher must be a little bit suspect as well.

Kobo rejected nearly 45% of books submitted to its self-publishing platform in 2025, about 80% of which were based on suspected AI-generation. Self-published fiction ISBNs jumped from 306,781 to 477,104 in a single year, and while nobody can prove that spike was entirely AI-driven, it’s not a stretch to connect the dots. The flood is real.

Amazon’s approach is telling too. KDP now requires authors to disclose AI-generated content during upload, but they don’t reject books for using AI and they don’t show readers the disclosure. The distinction they draw is between AI-generated (AI produced the text) and AI-assisted (you used AI for brainstorming, grammar, editing), and only the first one requires disclosure. They’ve ramped up enforcement in 2025 and 2026 but the message is clear: use AI if you want, just tell us, and make sure the quality is there.

And readers do care about human connection. A YouGov survey found 54% of literary fiction readers would feel “much less fulfilled” if they learned a book was AI-authored. The 2026 State of Reading Report found that personal recommendations from people readers know have overtaken ALL other discovery sources. People want a real person behind what they’re reading.

If all you’re doing is prompting ChatGPT and hitting publish, the market is already turning against you. That part is accurate.

But that is a very specific kind of AI use. And it is NOT where the technology is heading.

The Speed of Advancement Nobody’s Accounting For

I feel like I’m learning 50 new things a week just trying to keep up with how fast content creation tools are advancing (and I do this full time). Eighteen months ago, AI writing tools were simply generic text generators.

Now I’m drafting a seven-book fiction series and the tools are entirely different. That’s why I decided I COULD write fiction with AI. I’m about to maybe bore you with some literary jargon and requirements, but stick with me. There are now AI writing tools built specifically for fiction that track character continuity across 100,000-word manuscripts, maintain relationship progression arcs for romance, and track clue revelation pacing for thrillers. The scope of what I’m building would have been unmanageable with AI even a year ago, and the tools keep getting better month over month.

It is predicted that by 2030, running a large language model (like ChatGPT or Claude) will cost providers over 90% less than it does today. That means better tools, available to more people, for almost nothing. The barrier to entry is approaching zero.

Amazon Web Services estimates that 57% of online content is ALREADY generated or translated by AI. Whether you find that exciting or unsettling, the direction is clear and it is not reversing.

Detection Won’t Sort This Out

I know the instinct is to think detection will solve this. That we’ll build better detectors and the problem goes away. The data says otherwise.

Independent testing in 2026 shows AI detector accuracy averages 73% across eight major tools in real-world conditions. That’s for raw, unedited text. When a human edits the content (which is how most people actually use AI), no detector exceeded 62% accuracy. After a few passes through a paraphrasing tool, no detector consistently identified AI content AT ALL.

Human accuracy at identifying AI-written text? Nineteen percent. That’s indistinguishable from random chance. For real.

Every time detection tools improve, the models release updates that produce more human-like text. The statistical gaps that detectors rely on keep narrowing. Even the Authors Guild acknowledged that no reliable detection method currently exists for vetting manuscripts. Their “Human Authored” certification is on the honor system and you get it by signing a form. That’s it.

I know what this means for me personally. I use AI in my writing workflow. Someone could point at my work and make the same accusation that took down Shy Girl. I’ve thought about that. And I’ve made peace with it, because I know the theology is mine, the voice is mine, the discernment is mine, and the 100+ guardrails I built to protect my writing are mine. If someone runs my work through a detector and it flags, that doesn’t change what I know about how it was produced. But I understand why that’s a vulnerable position to be in. If you’ve spent years developing your craft and someone can produce something comparable in a fraction of the time now, I can see how that sucks.

But the frustration doesn’t change the trajectory of where AI writing is headed.

AI-Assisted Writing Done With Integrity

My daily workflow looks like this.

I built a guardrails document with over 100 specific patterns to avoid and use. I trained AI on my voice using transcripts of how I actually talk. I review and edit every single word of every piece of content before it goes out. The result is content that sounds like me, carries my thinking, and reflects what I actually believe. AI accelerates the production, but what you’re reading when you read my work is my mind, my convictions, and my voice. Every time.

My eschatology reading experience made this concrete for me. Those books were good because whoever created them understood the subject deeply enough to curate, organize, and present it in a way that was genuinely useful, regardless of what produced the first draft. What I cared about as a reader was the theology. And whoever was behind those books clearly knew their eschatology.

Among fiction authors who use AI, only 11% use it to generate publishable text. The vast majority use it for brainstorming content creation ideas, research, and finding the right phrasing. A Bynder study found that when readers were shown two articles without knowing which was which, 56% preferred the AI-assisted version. Genre fiction readers rate well-edited AI-assisted work as comparable to human-authored category fiction.

The line between “assisted” and “unassisted” is disappearing. And as the tools improve, it will disappear entirely.

Where I Think This Goes

Here’s my prediction.

Within five years, virtually every published book will involve AI at some stage of the process. The authors who refuse to use it will become the rare exception, the same way someone who refuses to use a word processor is the exception today. The tools will simply become so embedded in the writing process that asking whether someone “used AI” will stop meaning anything.

Think about it - spell check is AI. Autocomplete is AI. Those functions are already accepted and not even considered in this argument.

Ninety-seven percent of content marketers plan to use AI for content creation in 2026. That was 64.7% in 2023. Gartner predicts that by 2027, 75% of hiring processes will include testing for AI proficiency. The skill is becoming a baseline expectation.

Gartner and OpenAI are both projecting that by 2030, AI systems will function as collaborative partners that understand your project history, your audience, your voice patterns, and your strategic goals. I’m already seeing early versions of this in how I use AI for my fiction book series and the Kingdom entrepreneurs book, and it is changing how I think about what’s possible for a single author with a full teaching calendar and a life that doesn’t stop for content schedules.

The question “did you use AI?” will sound the way “did you use Google for research?” sounds today. A question that means nothing in practice because the answer is obviously yes.

This is part of why I’m writing the AI for believers book. Because the faith community needs a framework for thinking about this that isn’t built on fear or blind adoption. We need discernment. We need clarity about what AI should and shouldn’t touch. And we need it before the tools outpace our ability to steward them well.

To the Person Who Left That Comment

If we were sitting across from each other, I wouldn’t argue with you. I’d tell you I understand exactly where you’re coming from. The slop is real. The flood of low-effort content is real. The impulse to protect what’s human and authentic in creative work, I share that impulse completely.

But I’d also tell you that the technology you’re judging today is not the technology that will exist in two years. Or five. Or ten. And every month you spend waiting for AI writing to become “unmarketable” is a month someone else spent learning to use it well.

If you’re a business owner making decisions about content creation and business growth right now, the question that actually matters is whether you have something real to say. Expertise and conviction that come from actually doing the work, not performing it. And personal branding grounded in who you actually are.

Done well, AI-assisted content is getting better every single day. I see it in my own work and in the work of people I respect who are building with these tools seriously and with real guardrails in place. Get it wrong (no expertise, no voice, no guardrails), and readers will reject it.

But the idea that AI writing itself will become unmarketable? Not going to happen.

And yes, this article was written with AI assistance, using the exact guardrails and voice-training process I just described. If you couldn’t tell, that’s the point.

If you want to understand how AI is reshaping content creation and online business, and how to make money online without compromising your voice or your integrity, the AI Revolution Secrets training is where I’d start. It’s free, it’s practical, and it gave me the framework for deciding what AI should touch and what stays human. Everything I’ve built since came from that foundation.

Register for the free training here

I launched AI with Leah on Substack on December 21st, 2025. As of today I have 450 subscribers, 814 followers, 5 paid subscribers, 60 published articles, and $580 in total revenue. I wrote 30 articles in my first 30 days.

Those numbers are small. I’m sharing them anyway because the polished version of early-stage growth that most people publish after they’ve already made it is useless to someone who is actually in the middle of building.

The Part Nobody Sees

There was a stretch in late January where I seriously questioned whether any of this was going to work. I had been publishing daily for over a month. The subscriber count was crawling. I’d look at the numbers every morning and the line was barely moving. 67 new subscribers in all of January. I was putting out some of my best work and the engagement on certain pieces was brutal.

I published “AI Won’t Fix Your Business” and got a 4.9% engagement rate. I wrote “The Dirty Secret Behind Most Influencer Engagement” and got 3.8%. These were articles I was proud of. Articles I thought would land. And the response was basically silence.

That’s the part nobody tells you about building a Substack. The compounding everyone talks about is real, but there’s a long stretch before you can see it where you’re just publishing into what feels like a void and choosing to believe the math will eventually catch up. For WEEKS, that’s all it was. Publishing and believing.

I kept going because I didn’t have a better option. I had committed to this platform publicly. I had 120,000 words of unpublished content that proved I could think but not finish. AI had finally solved the finishing problem. And quitting after 30 days would have confirmed every fear I had about myself as a writer.

And then March happened.

The Growth Curve

December 2025: 28 new subscribers (launched Dec 21st, so really 10 days) January 2026: 67 new subscribers February 2026: 77 new subscribers March 2026: 256 new subscribers April (first 11 days): 25 new subscribers

March was the breakout. 256 new subscribers in a single month after averaging about 70 for the two months before it. A 232% increase month over month. And I can tell you exactly what caused it.

Substack Notes Changed Everything

58% of my total subscribers came from Substack Notes. FIFTY-EIGHT PERCENT.

Not from Pinterest, which has 200,000+ audience and drove 2 direct subscribers. (Two. The number after one. LOL.) Not from Instagram. Not from Facebook (which drove 34, the second highest social source). Notes drove 265 of my 453 tracked subscriber additions.

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I posted that as a note on Substack. 178 likes, 91 comments, and 12 restacks. One person told me I should list the LLM (the AI language model) as the author and put myself in the acknowledgments as “prompt creator.” Another said it’s “false or illegitimate” to use AI for writing. Period.

The objections people have to AI-assisted writing have a lot more to do with ego than they do about writing. The fight is over who gets to call themselves a WRITER.

Every Creative Field Has Had This Fight (And the Gatekeepers Lost Every Time)

Music producers went through this exact thing when digital audio workstations replaced tape recording. “Real musicians record live.” The old guard said Pro Tools was cheating and auto-tune was an insult to talent. The whole idea that anyone could make an album now was treated like a threat to the craft. And then Billie Eilish recorded an album that won five Grammys in her brother’s bedroom using Logic Pro and a USB microphone. Nobody argued about whether the tools were legitimate after that. The industry moved on. The gatekeepers found something else to complain about.

Architects went through it when CAD replaced hand drafting. “Real architects draw by hand.” There were entire professional organizations that resisted the shift. They said the soul of design was in the hand-drawn line, that something essential was lost when you let software do the calculations. Every new building you’ve walked into in the last 20 years was designed in CAD. And the buildings got more ambitious, not less.

This is what happens every single time.

A tool arrives that makes the work faster and more accessible. The people who built their careers doing it the hard way feel the ground shift. They frame the new tool as cheating because that’s easier than admitting the game changed. They gatekeep for as long as they can. And then the tool becomes standard and nobody remembers the argument.

Writing is going through this right now. AI is the tool. The arguments are identical. “Real writers don’t need it.” “You’re not a real author if AI had any part in it.”

The premise of the objections are the same. It’s a different decade and a different tool, but it’s the same motivation and fear of losing relevance underneath. It’s being driven by the same ego.

But the gatekeepers are going to lose this one too.

What the Loudest Critics Are Actually Protecting

Some numbers first so you understand what’s actually happening in the field.

45% of authors surveyed by BookBub (over 1,200 of them) are already using AI in their workflow. Publishers Weekly found a 31% productivity increase among authors who use it. The global market for AI writing tools is projected to hit $47 billion by 2034.

Here’s the biggest and most telling stat of them all. Of the 45% using AI in their writing process, 74% of them don’t tell their readers about it.

Nearly half of working authors are already doing this. Most of them quietly. (Which tells you everything about the climate right now.) And the reason they’re quiet is because admitting you use AI feels like professional suicide because most of the publishing houses and authors associations are taking a hard line on AI generated content right now. In early 2026, Hachette pulled a contracted horror novel over suspected AI use. The Authors Guild launched a “Human Authored” certification program. And virtue-signaling writers are putting anti-AI badges on their profiles like it’s a political campaign.

The writers who are most vocal about AI-assisted writing being illegitimate are almost always writers who built their audience on craft. They spent years developing their skill and earned their position through a process that was slow, difficult, and exclusive to people who had the time, the discipline, and the cognitive wiring to do it the traditional way.

AI changed the economics of that overnight. And that scares them.

A person who thinks beautifully but writes slowly can now produce finished work. Ministry leaders who preach with fire on Sunday but can’t get a blog post written during the week can finally get their ideas into a format that reaches people beyond the room. People who were locked out of the writing world because their brains didn’t work the “right” way can now participate.

That means more competition and more voices in the space that weren’t there before. And if your entire identity is built on being one of the few people who could do this well, that’s a problem for you.

I clearly have strong feelings about this. Wrapping your competitive anxiety in a moral argument about the “sanctity of writing” is ridiculousness. If your writing is good, AI-assisted writers entering the space shouldn’t threaten you. Period. And if it does threaten you, it’s worth asking yourself if your real value was actually the writing itself or just the fact that fewer people could do it and it made you feel special.

The Blank Page Bottleneck

I’ve talked about how my brain works in previous articles, but here’s a recap in case you missed it. I process ideas fast. Frameworks form fully in my head and I can effortlessly explain them out loud and have someone walk away understanding completely. Everything breaks down because my brain is thinking three ideas ahead of the paragraph I’m trying to finish so I get frustrated and then just stop.

I spent YEARS thinking that was a discipline problem. It wasn’t. And once I figured out why, everything changed.

Writing involves two completely different processes. Getting ideas onto a blank page from nothing (generating) and shaping a draft that already exists into something good (editing). My brain is wired for developing ideas quickly and also for editing. I work best when I can explain via talking (typically in an audio to text transcribing tool). Those ideas are usually delivered in a way I would explain it to someone and not necessarily in linear steps or in a format that makes sense simply converted to writing. The beauty of working with AI is that it can take my ideas and thoughts, organize them and get them onto the page for me. Then I can push back, cut, restructure, and refine until it sounds exactly like me. I’m fast at that part and I genuinely enjoy it. Generating the first draft is where my brain stalls, and it always has. The ideas are there. Getting them into linear written form from scratch without help is the part that breaks down.

AI eliminated the blank page. It gives me a draft to edit. And editing is where my actual skill lives. The blank page was just a bottleneck that had nothing to do with whether I had something worth saying or not.

I’m not the only person this is true for. In a world of increasing neurodiversity, the cut and paste playbook no longer works.

The question the anti-AI crowd never answers is this: if the thinking is original, the voice is real, the editing is rigorous, and the final product is indistinguishable from hand-written work, what exactly was lost by not staring at a blank page for four hours first? Nobody has given me a good answer to that.

The closest thing to a real argument I’ve heard is the idea that the struggle of writing IS the thinking. That when you’re fighting to find the right word or restructuring a paragraph for the fourth time or deleting everything because the argument just isn’t working, that friction is what sharpens the ideas. And that if you bypass all of that with AI, the thinking never fully develops.

And look, I take that seriously. And for people who type a topic into ChatGPT and publish whatever comes back, the criticism holds. That work IS shallow because the thinking was shallow.

But in my process, the thinking happens BEFORE the draft. I know what I’m arguing, who I’m talking to, and what I want the reader to understand before Claude writes a word. Then the draft comes back and I fight with it. Sections get restructured. Entire paragraphs get rejected. I rewrite openings three and four times and add the examples and personal details that make the piece mine. The struggle is still there. I’m just struggling with a draft instead of a blank page. The intellectual work is all still happening. The only thing that changed is that the production bottleneck is gone, and the thinking can actually reach the reader now.

If the struggle itself was the point of writing, we’d still be chiseling stone tablets.

For these people, AI is an accessibility tool. Calling it cheating is the same as calling a wheelchair cheating because the person using it can’t walk as fast as you can. And I don’t hear anyone in the anti-AI writing crowd getting riled up over that! LOL.

See for Yourself

This is from this article. The opening of the Blank Page Bottleneck section.

What Claude wrote:

“I’ve talked about my own brain before. I process ideas fast. Frameworks form fully in my head. I can explain them out loud and have someone walk away understanding completely. But sitting down to write them in linear, long-form prose? My brain is three ideas ahead of the paragraph I’m trying to finish before I even get through the first section.

I spent YEARS thinking that was a discipline problem. It wasn’t. My brain just works faster than the traditional writing format allows for. AI solved that by giving me a structured draft I could edit instead of a blank page I had to fill. Editing is a completely different cognitive task than generating from scratch. My brain can edit all day.”

What I published:

“I’ve talked about how my brain works in previous articles, but here’s a recap in case you missed it. I process ideas fast. Frameworks form fully in my head and I can effortlessly explain them out loud and have someone walk away understanding completely. Everything breaks down because my brain is thinking three ideas ahead of the paragraph I’m trying to finish so I get frustrated and then just stop.

I spent YEARS thinking that was a discipline problem. It wasn’t. And once I figured out why, everything changed.

Writing involves two completely different processes. Getting ideas onto a blank page from nothing (generating) and shaping a draft that already exists into something good (editing). My brain is wired for developing ideas quickly and also for editing. I work best when I can explain via talking (typically in an audio to text transcribing tool). Those ideas are usually delivered in a way I would explain it to someone and not necessarily in linear steps or in a format that makes sense simply converted to writing. The beauty of working with AI is that it can take my ideas and thoughts, organize them and get them onto the page for me.”

Claude’s version was competent but generic. And Claude only produced that because I had already loaded it with my voice sample, my guardrails, and detailed context about who I am and how I write. Without all of that, the output would have been generic slop. My version added the frustration (”so I get frustrated and then just stop”), the audio-to-text detail, the distinction between generating and editing as separate processes, and the explanation of HOW AI actually fits into my workflow. That’s what editing a draft looks like.

“Just Make Sure the Author Is the LLM”

Someone in my comments told me that if I write a book using AI, I should list the LLM as the author and put myself in the acknowledgments section as “prompt creator.” Aren’t keyboard warriors fun?

That comment reveals exactly how little most people understand about what AI-assisted writing actually involves.

A “prompt creator” types “write me a blog post about productivity” and copies what comes back. That’s what this person thinks I do. That’s rubbish. Here’s what actually happens when I write.

Before AI writes a single word for me, I’ve already decided what I’m writing about, who it’s for, what problem I’m addressing, what point I’m making, and what I want the reader to walk away thinking. That’s the work, and AI cannot do it for me. If I skip that step, the output is garbage every time. I’ve proven that to myself more times than I want to admit.

Then there’s the system I built. A voice sample I created by recording myself talking for ten minutes and transcribing it so Claude knows how I actually sound. A guardrails document with 98 specific patterns that AI must avoid. Things like mirrored contrast phrases and performed vulnerability, plus dozens of structural and tonal tells I identified by spending months being annoyed by the output and writing down exactly what was wrong with it. On top of that, there are voice rules that define how my content should sound, a personal lexicon of words and phrases I favor and ones I refuse to use, and a set of required patterns that every piece of content has to meet before it publishes. That system took a long time to build and I refine it constantly.

When Claude gives me a first draft, I read every word and push back on phrasing that doesn’t sound like something I’d say out loud. Entire sections get rejected. Then I add the personal stories, the real examples, and the specific details that only exist in my head. The piece goes through a multi-pass editing process that catches AI patterns at the sentence level. And I read the final version out loud to make sure it sounds like me.

I have 98 editorial rules, a voice training system, a multi-pass audit process, and the final creative authority over every word that publishes under my name. The person who left that comment has a snarky one-liner and zero understanding of the process they’re criticizing.

The Part I Don’t Talk About Enough

My process isn’t perfect. If I only talk about the wins, I’m doing the same thing the AI slop creators do, just from the other direction.

Early on, before the guardrails were tight, I published pieces that had AI fingerprints I didn’t catch. Phrases that were too polished, structures that were too symmetrical. I look back at some of those early articles and I can see it. A reader who knew what to look for could see it too.

That’s actually why the guardrails document exists. Every pattern in that document started as a mistake I made. Something Claude produced that I let through because I was tired, or moving too fast, or hadn’t trained my eye to catch it yet. The 98 patterns didn’t appear overnight. They were built from failure, one cringe at a time. (And there were a LOT of cringes.)

If you’re going to use AI to write, you WILL publish things you wish you hadn’t. You’ll miss patterns. You’ll let something through that doesn’t sound like you and someone will notice. The question is whether you learn from it and tighten your system or just keep pretending the raw output is fine.

I tightened. And I keep tightening. That’s the goal.

What I’m Doing Next

I’m writing a book right now using this exact process. Same Claude projects, same guardrails and voice training, same editorial standards I use for every Substack article. And I’m documenting the whole thing as I go.

I’m putting together a working experience where you write your own book alongside me, using my guardrails and my process, and come out the other side with real progress on your manuscript. More details on this are coming soon.

If that interests you, make sure you’re subscribed so you don’t miss it or send me a DM and let me know you are interested.

Write well. Be honest about how you do it. And stop letting people who’ve never examined their own process tell you yours doesn’t count.

I’ve written about AI-assisted writing before. If you missed those articles:

What AI-Assisted Writing Actually Is (And Why the Debate Around It Is Missing the Point)

I Have Hundreds of Thousands of Words Written and Nothing to Show for It (Until AI)

If you want the system behind everything I write, the AI Writing Guardrails is the exact framework. 98 patterns to avoid, voice rules, a guided builder for creating your own custom rules, and a 5-pass editing process.

Because “make it sound more human” was never going to work.

Self-Guided or Custom Build options available.

guardrails.aiwithleah.com/info

Happy Easter.

All of it, the blog, the podcasts, the products, the systems, started with something dying first.

The Life That Had to End

I ran a multiple seven-figure online coaching business in the wealth consciousness space. From the outside it looked like everything was working. Revenue was high, programs were selling out, and the audience kept growing. I was living the “dream” life, living in a multi-million dollar home on the beach in Bali. I had the clothes, the jewelry, the glam team. I was also deep in the New Age and the occult, operating in frameworks I believed were helping people create financial breakthroughs. And they were, in the short term.

But I was miserable. My marriage was degrading. My children were being raised by nannies. I had gained over forty pounds and was living in chronic pain. I was chasing the next $100,000 month and the next sold-out program while my body and my family were breaking down around me.

I didn’t realize at the time that for all the power and influence I was gaining while operating in the kingdom of darkness, there was a price being paid. That price was my health, my marriage, my presence with my children, and ultimately my own soul.

By the summer of 2023, everything was falling apart. I was separated from my husband, and I had been away from my children for five months in another country chasing the next million dollar business idea. I had chronic inflammation, thyroid disease, and migraines that lasted five to seven days. I remember sitting in an apartment in Porto, Portugal. I had laid out large pieces of white paper on the floor because I like to whiteboard and map things out. I wrote down everything happening in my business, everything happening in my life, all the programs I was preparing to launch, the new business. And as I looked at it, I realized I didn’t want to do any of it.

I fell to my knees and cried out to God. I told Him I didn’t know what I was doing. I told Him I needed help. I surrendered all of it. Even my children, if that was truly what was best for them.

That was the day my old life died.

What Came After

The next day I woke up and heard from the Lord clearly for the first time in a long time. He told me to go carnivore, to completely change my diet, and to stop drinking alcohol entirely. I did. Within two weeks, about seventy-five percent of my pain was gone. The inflammation was leaving my body and my brain. With that physical clarity came spiritual clarity I hadn’t experienced in years.

Over the next year I began reading the Bible seriously. I reconciled my marriage. I moved back in with my children. I walked away from my online coaching business completely because I could no longer participate in New Thought or New Age frameworks. The Lord taught me about provision (I don’t use the word abundance anymore because it belongs to a worldview I left behind). The difference matters.

I shifted into network and affiliate marketing, a model I had always respected because it allowed me to help people build income without spiritual compromise. After another year, I finally gave my life to Jesus. I had resisted Him for a long time, and the moment I stopped, everything changed.

The Resurrection Pattern

I’m telling you this on Easter because the pattern of my life mirrors the pattern we celebrate today. Something had to die for something new to live. The old business, the old frameworks, all of it had to go into the ground.

What came out of that death is everything I’m building now. Especially today, I want you to see what that looks like.

What Grew From the Ashes

The coaching business that died? That’s where AI with Leah came from. I spent years unable to finish long-form content. 120,000 words across two unpublished books sitting in a drawer. God kept those New Age manuscripts from ever reaching the world (thank God), and then gave me a tool that matched how my brain actually works. 50+ articles published in 90 days. A Pinterest account at 200,000 audience. Over 430 Substack subscribers. A book on AI for believers currently being written. All of it built with AI systems, one VA, and a laptop in Bali. After years of finishing nothing, I have more published work than I know what to do with.

The Cul✞ure Cast grew directly out of the years I spent in deception. When you’ve lived inside a false system and watched it operate from the inside, you learn to recognize the mechanisms. Cultural analysis grounded in Scripture, naming what’s actually happening behind the headlines. I couldn’t do that show if I hadn’t lived in the dark first.

My husband came to Christ a few months after I did and launched his own business, Joyful Jesus Apparel (shameless plug because I’m so proud of him)!

https://joyfuljesus.life/

We’re building together now. That floor in Portugal where I surrendered my marriage to God didn’t just save the relationship. It produced a family operating in the same Kingdom for the first time.

And then there’s Faith on the Fringe, which came from the two years I’ve spent studying Scripture obsessively.

Faith on the Fringe

I’m co-hosting this with Malaine Lea Butler and we launch April 27th with four episodes dropping on day one.

The tagline is “Spirit-Filled. Bible-Obsessed. Slightly Unhinged.” Slightly unhinged because we’re going into the parts of Scripture that most churches won’t touch. The Divine Council. Genesis 6. The Nephilim, the Watchers, fallen angels, giants. Pre-flood history. Spiritual warfare with real scriptural backing. Fulfilled eschatology. All of it examined through a Kingdom-forward lens rooted in the finished work of Christ and the authority of believers. We explicitly reject fear-based dispensationalism and rapture theology.

This space in podcasting is overwhelmingly male-dominated. Shows I love and respect like Blurry Creatures and Into the Supernatural are doing incredible work, but there is no female co-hosted show going this deep into supernatural biblical theology with this level of scriptural grounding. Malaine and I looked at that gap and decided to fill it. We bring deep theological roots, humor, and the kind of long-form conversations (one to two hours, with guests) that make you want to open your Bible and start digging. Serious scholarship with real scriptural grounding, and we’re having fun doing it.

It will be available everywhere podcasts are streamed and on YouTube on April 27th. We are also launching a Substack alongside it for deeper conversations and community interaction. I’ll share more about this as we get closer.

What’s Still Coming to Life

The resurrection pattern doesn’t stop. It keeps producing.

I’m working on a series of historical biblical fiction books with two of my besties in Christ. It’s called House of Eden. Think viral BookTok fantasy meets the Bible. Supernatural worldview, and real scriptural foundation, written to bring people to Jesus through story. I am SO excited about this project. It’s a collaboration that feels like exactly the kind of thing God puts together when you stop trying to plan everything yourself.

The AI book is being written. The fiction series is taking shape. Faith on the Fringe launches in three weeks. My husband is building Joyful Jesus Apparel. And I’m answering new assignments to reach people in ways I couldn’t have imagined three years ago when I was on that floor in Portugal.

The resurrection pattern is how God actually works, not just on Easter but in the lives of people who surrender to Him. He lets the old thing die completely and builds something entirely new from the foundation up. And He keeps building.

Three years ago I was a miserable, sick, absent mother running a business built on frameworks from the wrong Kingdom. My family is together now, my marriage is restored, and everything I’m building serves the same mission: stewarding truth, protecting identity, and equipping people with tools and theology that produce real fruit.

I didn’t plan any of this. I surrendered and then I obeyed, one step at a time.

Happy Easter. He is risen. And He’s still making dead things come back to life.

March 2026 was the month AI stopped being experimental. Four things happened in the span of 30 days that, taken together, signal a permanent shift in how this technology affects your business and your daily life.

The Layoffs Are Real and They’re Explicitly About AI

In late February, Block (founded by Jack Dorsey, the guy who created Twitter) cut roughly 4,000 employees, nearly 40% of its workforce. Dorsey was blunt about why. He said the growing capability of AI tools to perform a wider range of tasks made the cuts necessary. That’s a CEO saying AI can do what these people were doing and he doesn’t need them anymore.

On March 11, Atlassian (the company behind Jira and Confluence, project management tools used across the tech industry) cut 1,600 employees in a single morning. Over 900 of those cuts came from research and development. CEO Mike Cannon-Brookes said the layoffs were necessary to “self-fund further investment in AI.” Self-fund. That means they’re firing people to pay for the GPUs (the specialized chips that power AI) and data centers required to compete.

Meta cut hundreds more around the same time. Same story. Redirect the money into AI.

Companies explicitly cited AI and automation as the reason for over 9,200 tech layoffs in Q1 2026. That number jumped from less than 8% in 2025 to over 20% this quarter. The stock prices of these companies went UP after the announcements. The market is REWARDING companies for cutting humans and investing in AI.

I’m living this right now. I run my entire business with one VA working 20 hours a week and AI systems handling the rest. The workload I carry today would have required a full team two years ago, and now it runs on systems, clear thinking, and AI that I’ve trained to work the way I need it to.

OpenAI Killed Sora and a $1 Billion Disney Deal Died With It

On March 24, OpenAI shut down Sora, its AI video generation tool. Six months after launching it to the public, they pulled the plug.

The Wall Street Journal investigation told the real story. Sora was burning through roughly $1 million every day in compute costs (some analysts estimated the peak was significantly higher). Total lifetime revenue from the app was $2.1 million. The math was never going to work. User counts peaked at about a million and then collapsed to under 500,000. Downloads dropped 66% between November and February.

Disney had committed $1 billion to a partnership built around Sora. They found out it was being shut down less than an hour before the public announcement. One hour. The deal died immediately. No money had changed hands. (Imagine being Disney and finding out your billion-dollar AI partnership is dead via a press release you didn’t know was coming.)

I’d already moved away from Sora months ago because the output was inconsistent and there are much better tools available. I use Veo 3.1 and Grok Imagine for my video content now. So the shutdown didn’t affect my workflow at all. I think it was actually a smart move by OpenAI. Their applications chief told employees they “cannot miss this moment because we are distracted by side quests,” and she was right. OpenAI was pouring resources into a video toy that couldn’t pay for itself while Anthropic was quietly winning enterprise customers and developers with Claude.

But most creators and businesses weren’t paying that kind of attention. They were planning workflows, content strategies, and even business models around Sora. And it vanished overnight.

I’ve seen this pattern before in online business. The platform you build on is the platform that controls your future. The lesson from Sora is the same lesson I learned years ago: if a tool can’t sustain itself financially, it will disappear. Build on tools that have actual revenue models and staying power, because the ones that can’t pay for themselves won’t be here next year.

Social Media Addiction Just Became a Legal Liability

Two verdicts landed in the same week in March, and they may reshape how every content creator and business owner uses social media.

If you want to know more and what to do about it… plus get access to the full AI in Action LIVE recordings (7 days of structured AI training) and the weekly AI with Leah: Unfiltered podcast become a paid subscriber!

Read more

One of the most common reasons people give me for not using AI is privacy. They don’t want to “give their data away” and they don’t trust the companies handling it.

So first let me just say that I get it and those concerns are valid. But here’s what really needs to be said: that ship sailed a long time ago. Years ago. Possibly decades.

If you’ve EVER typed your Social Security number into a website or applied for a credit card online, your data left your hands a long time ago. If you carry a smartphone right now, it’s still leaving. And the idea that avoiding AI is somehow protecting you is one of the biggest misconceptions keeping people stuck right now.

The Reality Nobody Wants to Sit With

There are over 4,000 data brokerage companies operating worldwide. It’s an industry worth over $250 billion and their entire business model is collecting your personal information and selling it to other companies.

One company alone, Acxiom (now LiveRamp), holds profiles on 2.5 billion people with up to 11,000 data points per person. Think about those numbers for a second. That’s insane right? Those data points include name, address, income range, political leanings, health conditions, what you buy and how often. They know your family members and your estimated net worth. And all of it is sitting in a database you’ve never seen, being sold to companies you’ve never heard of.

That’s ONE company out of thousands.

98% of Americans have personal data exposed by at least 35 different data brokers. The average person (aka YOU) has around 300 pieces of information publicly accessible right now and available for purchase or sometimes for free, on sites most people don’t even know exist.

Your Phone

A Vanderbilt University study found that a stationary Android phone with only Chrome running in the background sent location data to Google 340 times in a single day. That’s a phone just sitting on your nightstand doing nothing, and it’s STILL pinging your location every few minutes.

When they tested a phone during normal daily use, that number jumped to 450 location transmissions per day (roughly ninety per hour). And a New York Times investigation found that some apps logged a user’s location up to 14,000 times in a single day.

Your phone knows where you sleep and where you work. It knows what time you leave in the morning, what route you take, and whether you stopped at the gym or skipped it. It tracked the doctor you visited last Tuesday and how long you were in the office. Your phone knows MORE about your daily life than most of the people in it (and that’s not an exaggeration).

82% of iOS apps collect private user data. The location data industry alone is a $12 billion market. Companies openly advertise that they’ve collected location data from 25% of all US adults. And 62% of Americans believe it is straight up impossible to go through daily life without companies collecting their data.

They’re right. It is.

The Part People Miss About AI Specifically

When someone tells me they won’t use AI because of data privacy, I know they haven’t really thought it through or they just don’t have the information. Because I know they will type their credit card number into an online store, share their location with a food delivery app, give their Social Security number to a tax filing website, let their phone track them 450 times a day, and use social media platforms that literally sell their behavioral data to advertisers. But typing a business question into ChatGPT? That’s where they draw the line.

I use AI every single day knowing everything I just told you about data collection. I paste business strategy into Claude, draft content, plan launches, build systems. I do it with my training toggles turned off, my settings managed, and my eyes wide open. Because I did the math. The data was already out there. The only thing I’d gain by avoiding AI is falling behind.

For a lot of people, “privacy” is not actually the issue. It’s the reason they give, and it sounds responsible and informed. Nobody argues with someone who says they’re concerned about data privacy. But underneath that concern, the real thing happening is fear. Fear of the technology itself or fear of finding out how far behind they already are. And privacy becomes the permission slip to not engage, because it feels safe and it sounds smart.

I can’t even count how many times I’ve had this exact conversation. Someone tells me they’re worried about their data. I ask what phone they use. iPhone. Do they have location services on? Yes. Do they use social media? Yes. Have they ever filed taxes online? Obviously. So I ask, genuinely, what specifically about AI feels like the bigger risk? Because faced with all of that they don’t actually have an answer. Because it was never actually about the data.

The idea that AI is the thing putting your privacy at risk is like worrying about someone reading your diary after you’ve already published it on Substack.

So What Do You Actually Do?

The next part is for you if you feel a bit paralyzed but you know AI matters and that you need to learn it.

Go into ChatGPT. Settings, Data Controls, turn off “Improve the model for everyone.” In Claude, find “Help improve Claude” and toggle it off. In Gemini, look for “Gemini Apps Activity.” Takes about two minutes across all three. Do it once and move on.

Don’t paste client Social Security numbers or bank account details into AI prompts. That’s common sense, the same common sense you’d apply to any tool. And here’s the thing, your Social Security number is actually readily available to anyone that really wants it, but I wouldn’t be typing it online into ANY website if you are really concerned about data protection.

If you run a business, consider the paid or enterprise tiers. They have stronger data governance controls and typically don’t use your inputs for training. Frequently review what your AI tools have stored in their memory settings and clean out anything you don’t want there.

Those are smart, practical steps that take a few minutes. Do them and then get to work.

Because the cost of ignoring AI is compounding. The person next to you who started learning six months ago is producing in two hours what takes you eight. And that gap doesn’t stay the same. It gets wider every single month.

The people who will look back on this time and regret something? It won’t be that they used AI. It’ll be the months they spent deciding whether or not to start.

Two weeks ago, the internet decided Benjamin Netanyahu was dead.

It started during the second week of the Iran war. Iran claimed its missiles had struck Netanyahu’s office in Jerusalem. An AI-generated image circulated showing a man resembling the prime minister lying injured on rubble. Claims spread that his home had been bombed and his brother killed. Then Netanyahu went quiet. For days, no video appeared on his official channels. Only text statements. Tasnim News Agency, run by Iran's Islamic Revolutionary Guard Corps built their case on the gaps: no video for days, tightened security around his residence, and diplomatic visits that got quietly postponed. The conclusion was obvious (to the internet, at least): he was dead.

When Netanyahu finally resurfaced in a televised address on March 15, it should have ended the speculation. Instead, it made everything worse. A freeze-frame from the video appeared to show six fingers on his right hand, which is a common tell of an AI-generated video. Within hours, the theory shifted: Israel was faking his appearances using deepfakes.

He posted a video of himself getting coffee at a cafe in Jerusalem, joking about the rumors, spreading both hands to show five fingers on each. People analyzed the coffee level in his cup, scrutinized his ring, and claimed the receipt in the video was dated 2024. Elon Musk’s AI chatbot Grok was asked whether the video was real and declared it “100% deepfake,” calling it “a classic example of fake image manipulation.”

The video was real. A deepfake detection company GetReal Security, co-founded by UC Berkeley professor Hany Farid, performed a multimodal analysis of the audio, video, and facial and vocal biometrics. They found no signs of synthetic content or manipulation. The cafe confirmed he was there and the receipt people claimed was from 2024 simply looked that way in low-quality footage.

A real video of a living person, verified by the cafe’s own security cameras, was declared fake by the AI tool built to catch fakes. And millions of people had already made up their minds based on screenshots and speculation before anyone verified anything. This is the world we are currently living in.

The line between real and fabricated has collapsed. And nobody, not even the AI tools built to police that line, can reliably tell you what you’re looking at. It’s gotten insane out there.

Your Eyes Are No Longer Enough

This isn’t a political story.

What your eyes see on a screen can no longer be trusted as evidence of reality.

We have spent our entire lives operating on the assumption that video is proof. That if you watch someone say something on camera, they said it. That assumption is gone.

Pro-regime accounts have circulated AI-generated clips showing missile strikes flattening cities that never happened. People are cloning synthetic voices from a few minutes of audio sample. Entire social media personas are being constructed around people who don’t exist, with AI-generated faces, fabricated backstories, and real followings. One disinformation network generated material that received 145 million views in just two weeks, and almost all of it fabricated.

And the detection tools? The same AI chatbot that called Netanyahu’s real video “100% deepfake” turned around and told users that obviously fake footage of missile strikes was real. It got it wrong both ways, on the same platform, in the same week.

It Gets Me Too

I get caught ALL the time.

I’ve been studying AI obsessively for three years, using it every day in my business and teaching other people how to use it. And I still find myself looking at something online and genuinely not knowing if it’s real.

My actual process at this point looks like this: I see something that seems off, or seems too perfect, or triggers a reaction that feels engineered. I screenshot it and bring it to Claude with the question, “Is this real? Can you verify this?” Sometimes that works and Claude can identify the source, check the claim, and point me toward the original.

And sometimes it can’t and the AI doesn’t know either, or the tools flat out contradict each other (exactly like Grok calling a verified real video “100% deepfake”). So then I send it to my husband. He has great discernment and a completely different lens than I do, which means he catches things I miss.

When that’s still not enough, when my eyes can’t tell me and the tools can’t tell me and the people I trust are unsure, it goes to the Holy Spirit. For real. That is my actual workflow. I start with what I can see, move to the tools, bring in the people I trust, and when all of that fails (which it does more often than people realize), I go to the one source of discernment that doesn’t depend on any of it.

I’m telling you this because I don’t want you to think the answer is just “get better at spotting fakes.” I’m good at spotting fakes and I still miss them.

The Content We’re Creating

There’s another side of this that most people skip entirely. We talk about being deceived BY AI content, but almost nobody is asking the harder question: what about the content we’re putting out ourselves? If you’re using AI to write your posts, script your videos, or draft your emails, there’s a version of the same problem happening in your own workflow. Did you actually think it through, or did you just accept what the machine generated because it sounded good and credible and close enough?

I’ve had to confront this in my own work. There are times when AI produces something so clean that I almost let it go. It sounds smart and credible but when I read it again, I realize it isn’t really saying anything at all. People are putting out AI-generated content every day and passing it off as their own thinking. Their audience trusts them and has no idea the person behind it isn’t really behind it anymore.

Proverbs puts it plainly: “The simple believes everything, but the prudent gives thought to his steps.” That applies to what we consume AND what we create. Whether it’s a deepfake video you’re about to repost or a draft AI just wrote for you that you’re about to put your name on.

Discernment Is Older Than AI

The secular approach to this problem has value. Media literacy and fact-checking sites and detection software all have a place.

But they’re reactive. They respond to deception after it’s been created.

Discernment works differently. Discernment is a posture you carry into everything you consume and create. It’s the habit of testing what you encounter BEFORE you accept it.

John wrote to the early church: “Beloved, do not believe every spirit, but test the spirits to see whether they are from God, for many false prophets have gone out into the world.” That instruction wasn’t about AI. But the principle underneath it is exactly what this moment demands. Test what you’re seeing, reading, or listening to and don’t assume something is true because it looks credible or sounds familiar.

The world has always been full of things that sound true and aren’t. False prophets in the first century didn’t have deepfake software, but they did have charisma, confidence, and proximity to truth. They sounded close enough to the truth that people followed them. The false prophets of today still have charisma, confidence, and proximity to truth but they are more dangerous because AI and technology have also given them the tools to become viral and reach millions of people.

Living in It

I’m not going to hand you a five-step framework for spotting deepfakes. Anything I gave you today would be outdated in six months.

I’ve slowed my consumption waaaaaay down. Not because I’m disciplined (LOL) but because I’ve been burned enough times to know that when something provokes a strong emotional reaction, that’s exactly when I’m most likely to get fooled. The Netanyahu story spread as fast as it did because it was emotionally charged and arrived during a war. People reacted before they verified. I’ve caught myself doing the same thing, and the only thing that stopped me was the two-second pause where I thought, “Wait. Let me check this first.”

That pause is everything. And it’s not a sophisticated skill...just the willingness to not react IMMEDIATELY.

I’ve also learned that verification isn’t final. I go to the original source and look for statements on actual platforms, verified accounts, and official websites. If something only exists as a clip being shared out of context, I treat it as suspect. But I also know my verification tools can be wrong. So even after I check, I hold it loosely. That’s a weird place to live, but it’s the honest one.

And I protect my own output. Every piece of content I create with AI goes through my own review. Not just for accuracy, but for the deeper question: “Is this actually what I think, or is this just what sounded good?” My name goes on it and my credibility is attached to it, so that part doesn’t get delegated.

What Still Works

What AI cannot do is give you the ability to recognize what’s true. That comes from somewhere else entirely.

The people who have spent years in Scripture, learning to test what they hear against what God has actually said, have been training for exactly this moment without knowing it. They’ve been practicing a discipline that’s thousands of years old, and it’s never been more relevant than it is right now.

And that’s the good news in all of this. You don’t need a degree in computer science or a subscription to every detection tool on the market. You need the willingness to slow down, check what you’re seeing before you spread it, and protect the integrity of what you put out into the world. When everything else fails, go to God. He’s been the source of discernment long before any of this technology existed, and He’s not going anywhere.

I intend to keep practicing that. Imperfectly. Every day. I’d love for you to join me.

If this kind of thinking matters to you, subscribe. I write about AI, discernment, and building with integrity twice a week.

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I launched my Pinterest account and my Substack on December 21st, 2025. As of today, just over 90 days later, here are the numbers:

343,700 impressions. 14,500 engagements. 3,300 saves. 186,600 total audience. 10,700 engaged audience. 141 outbound clicks. And 419 Substack subscribers being fed partly by this pipeline.

(While writing this article I had to update these numbers four times because they kept growing by significant amounts day to day. That’s what compounding on Pinterest looks like in real time.)

The whole point of this Pinterest account is to drive people here to Substack. Every pin, every board, every keyword is designed to bring the right audience to these articles, to my digital products, to my digital products and courses, and eventually to AI consulting. Pinterest is the distribution engine. Substack is the destination. They work as an ecosystem, and the strategy I’m about to share is how I connected them.

I built the entire strategy with AI. The keyword research, the pin descriptions, the content themes, the board structure, the CTA rotation, and the daily pin scheduling system. I create all the content here in Claude and my VA schedules and publishes it using a strategy document I created. I’m currently working on automating the content creation piece through Claude Cowork, which will make the whole pipeline even faster.

About 30 days in, one of my video pins hit 60,000 impressions. It’s now sitting at 131,700. Shortly after that, one of my authority pins went viral (a simple quote graphic with no link) it now sits at 83,500 impressions. Those two pins put my account on the map. And the thing is, I almost changed the strategy before they hit. Pinterest doesn’t work like other social media. There’s no instant gratification. You post and wait and check analytics and wonder if any of it is working... for weeks. I’m used to seeing results fast. Sitting in a 90-day plan and trusting the process when nothing was visibly moving yet was one of the hardest parts of this whole build.

I’ve been promising this kind of behind-the-scenes content to my paid subscribers, so let’s get into it.

How This Started

I didn’t come to Pinterest with a background in Pinterest marketing. I came to it because I needed a distribution channel for my Substack content that wasn’t dependent on social media algorithms I don’t control. Content on Facebook and Instagram dies in 24 hours. Pinterest is a search engine where pins live for months, sometimes years, and keep compounding the whole time.

I spent about a week researching Pinterest strategy before I posted anything. I used PinClicks for keyword validation, Claude for strategy development and content creation, Gemini for research, Grok for additional data points, and Brave Search to cross-reference what I was finding. I also studied Simple Pin Media and Tailwind’s published research on what works in 2026.

The research phase was critical. Most people skip it and go straight to posting. And that’s why most Pinterest accounts don’t grow.

The Core Formula

Pinterest indexes keywords it recognizes. “Business,” “personal branding,” “leadership,” “make money online,” and “content creation” all have massive search volume on Pinterest. Most people in the AI space don’t realize that AI-specific terms have ZERO search volume on Pinterest. “AI income,” “AI business automation,” “ethical AI,” “AI business strategy,” none of these register in Pinterest’s search index.

So if I built my Pinterest strategy around AI keywords, nobody would ever find my content. The algorithm would have nothing to index.

The formula I developed solves this: Pinterest keyword + AI differentiator = search volume + brand positioning.

A pin titled “Build Your Personal Brand Using AI” indexes for “personal branding” at 76,000 monthly searches. The algorithm distributes it because it recognizes the keyword, and the person scrolling the feed immediately sees that this is AI-focused content. One title serves both.

I validated every keyword through PinClicks with real Pinterest monthly search volume data before I used it. I built a three-tier keyword bank. The heavy hitters (10,000+ monthly volume) like “leadership” at 99,000 searches, “personal branding” at 76,000, and “online business ideas” at 57,000 get front-loaded across the most pins. Mid-range keywords between 2,000 and 10,000 like “small business tips” and “content creation ideas” rotate in titles and descriptions. And a third tier of niche keywords under 2,000 provides supporting depth.

Every keyword in my bank was validated with real data. If PinClicks can’t confirm the volume, the keyword doesn’t go in the bank.

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Somebody is going to take your money this year for an AI course. The question is whether what they teach you will be worth it.

The AI education space right now is flooded. Everyone with a ChatGPT subscription and a Canva template is selling a course, a masterclass, a coaching program, or a “system” that promises to teach you how to make money with AI. Most of them are recycled free content repackaged with a price tag and a countdown timer.

I’ve spent a lot of money on AI education and evaluated dozens of offers. And I’ve watched people in my audience buy courses that taught them nothing they couldn’t have learned from a YouTube video. The reality is that difference between a good AI course and a waste of money is not always obvious from the sales page.

I’ve been in two AI mentorships over the last year. The hard truth is that everything I learned in both of them I could have learned for free on YouTube. The people running them did a good job of marketing and I liked them and their personalities. But when it came down to brass tacks, the substance wasn’t there. I stayed in one for a full year hoping it would get better, but replays didn’t get posted in a timely manner and I ended up paying a lot of money for content I barely used. The other one I clocked sooner and exited as soon as I realized it was overhyped and underdelivered.

Having said that, I’ve also purchased some $47 and $97 offers that were easily worth 10x what I paid for them. So the price tag alone tells you nothing. The filters I’m about to share with you are what I wish I’d had before I spent that money.

These experiences are also why I’m committed to creating and promoting offers that truly deliver value. I already publish two articles a week here for free. And for my paid subscribers, I’m making the full recordings from my 7-day AI in Action LIVE training available inside Substack. Seven days of structured AI training as part of your subscription.

But even with my own offers, I’d tell you the same thing. Before you hand over money to anyone for AI education, including me, run it through these filters.

Are They Actually Using AI in Their Own Business?

This sounds obvious but it eliminates about half the field immediately. A lot of AI educators are teaching AI as a topic. They’ve researched it, they understand the concepts, and they can explain the tools clearly. But they’re not running a business on AI systems. They’re not dealing with the reality of what happens when AI gets it wrong on a Tuesday night when you’re tired and the content needs to go out tomorrow. If their entire presence is AI education content and nothing else, ask yourself what they’re actually building with AI besides the course they’re selling you.

What Do They Say When AI Falls Short?

This is the fastest filter I know.

I produce rubbish drafts from Claude on a regular basis. Even with 98 documented guardrails loaded into every project. I talk about it publicly because pretending AI works perfectly would be a lie. If all you’re seeing from an educator is clean demos and smooth workflows, they’re hiding the messy parts to keep you buying.

The consequence of learning from someone who hides the limitations is that you have NO framework for what to do when AI gives you something wrong. And it will. You’ll publish content with errors, miss patterns that make your writing sound generic, or trust an output that damages your credibility. The educators who are honest about the real limitations of AI and who show you their own failures are the ones preparing you to handle yours.

The Framework Test

Prompt packs are everywhere. “100 ChatGPT Prompts for Your Business.” “The Ultimate AI Prompt Library.” These sell well because they feel actionable. You pay, you get a list, and you feel like you have something concrete. (You don’t, but it feels that way for about a week.)

The problem is that prompts without a system or a framework underneath them are like recipes without understanding how to use a knife or a stove. You end up buying the next prompt pack instead of understanding how to think about AI and build your own approach.

The courses worth paying for teach you how to build your own systems. That’s what separates education from a subscription to someone else’s shortcuts.

Are They Teaching Ethics or Ignoring Them?

AI raises real ethical questions. About authorship, about transparency, and about what should be automated and what should stay human.

If an AI educator never mentions ethics, boundaries, or discernment around responsible use, they’re giving you half the picture. And the missing half is the part that protects your credibility. That catches up with people eventually, usually in the form of lost trust, embarrassing corrections, or an audience that quietly stops engaging because something feels off about the content.

Follow the Money

I’m going to spend some time on this because I’ve been in business for over 30 years and online business since 2016. I know how incentive structures shape what people sell you.

When the course IS the business, the entire operation is built around launching and selling that course. The landing page will be immaculate and the email sequence will be dialed in. Testimonials curated, urgency manufactured (countdown timers, limited spots, bonuses expiring). And the actual content inside the course doesn’t have to be good for the business to make money. The revenue comes from getting you to buy. Once you’ve paid, the transaction is done whether the course delivers or not.

I’ve seen this model up close. I ran a multiple seven-figure coaching business. I know exactly how these launches work because I built them. The marketing machine can be so polished that the buyer assumes the product must be equally polished. That assumption is what the launch model is designed to create.

The other model looks different. Some AI educators make money USING AI in their actual business, and the education is an extension of what they do every day. The course exists because the system works and they’re teaching what they built. If their system doesn’t work, their business doesn’t work. They have skin in the game beyond the sale and that changes what you get.

I want to be fair here. Some course-first businesses are genuinely excellent. The model alone doesn’t make the education bad. But it does mean you need to look harder at what’s inside before you assume the sales experience reflects the learning experience. The shinier the launch, the more carefully I examine the curriculum.

How I Vetted AI Revolution Secrets (And Why It Passed)

I don’t put my name behind things lightly. AI Revolution Secrets by Miguel Carrasco is the one AI course I recommend, and I want to show you exactly why by walking through my own vetting process.

Filter 1: Is he actually using AI in his own business?

Miguel built and operates Captivation Hub, an all-in-one AI-integrated software solution for business operations that I’ve been using for three years. He also runs a company that integrates AI into HVAC businesses, and has several other ventures that all use AI as core infrastructure. He created AI Revolution Secrets to share how he uses AI to make money across all of his businesses. He teaches from what he built and continues to operate daily. When I looked at his content across platforms, I could see the systems in action.

He’s also offering a limited-time opportunity to white label his AI software, which is something nobody else in the AI education space is doing. That alone tells you how much he’s invested in the ecosystem beyond just selling a course.

Filter 2: Does he talk about limitations?

Yes. The training covers what AI can and can’t do. He doesn’t frame AI as a magic solution that runs itself. The 10-day micro-business blueprint is structured around implementation, which means you’re dealing with the real friction of building something from scratch.

Filter 3: Framework or prompts?

The course teaches AI cloning, seven income streams, and a complete micro-business system. These are frameworks you adapt to your situation. He’s not handing you a list of prompts and sending you on your way. The structure is designed to build capability. I could see that from the webinar alone before I ever paid for anything.

Filter 4: Does he address ethics?

The training teaches responsible use alongside the tactical implementation. This was important to me. I won’t recommend someone who teaches AI without addressing what it should and shouldn’t touch. Miguel’s approach includes human oversight and quality control at every stage of the system he teaches.

Filter 5: Where does the money come from?

This is where it got interesting for me. Miguel’s revenue doesn’t come exclusively from selling the course. He sells Captivation Genius, his AI software, alongside AI Revolution Secrets. So the success of the software IS dependent on how good the course is. If the training doesn’t deliver, people don’t use the software. If they don’t use the software, that revenue disappears. His business model forces the course to be good. That’s the kind of incentive structure I trust.

The one thing that surprised me:

The webinar is free. I went in expecting a pitch disguised as education (that’s how most webinars work). What I got was actual teaching. Substantive, specific, and useful even if you never bought the course. Miguel gives you enough to start building before you ever spend a dollar. That’s rare.

That was the moment I decided to put my name on it.

Register for the free AI Revolution Secrets webinar

What I’d Tell You Over Coffee

I’ve been on both sides of this. I’ve bought courses that were complete trash. I’ve also built and sold courses that generated millions. I know what a launch machine looks like from the inside.

The filters in this article exist because I’ve been burned and because I’ve watched other people get burned. AI education is going to be a massive industry for the next several years and most of what gets sold will not be worth the price. That’s just the reality of any gold rush.

Protect your money and your time. And protect your trust, because once your audience figures out you learned from someone careless, that reflects on you.

If you’re a paid subscriber, the full AI in Action LIVE recordings (all 7 days) are available inside your subscription. If you’re not subscribed yet, that alone is worth it.

Paid subscribers also get access to AI with Leah: Unfiltered, a weekly podcast where I go deeper on the topics I write about here.

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And if you’re ready to learn AI from someone who passes every filter on this list, register for the next AI Revolution Secrets webinar. It’s free and you can evaluate the teaching before you spend a dollar.

Register for the free AI Revolution Secrets webinar

I have over 120,000 words written across two books I never published. My brain processes things too fast and has a hard time sitting down to focus long enough to get anything into a finished format. I’ve had this problem my entire life. Ideas, frameworks, content, all of it lives in my head fully formed. Getting it onto a page in an organized, publishable way? That’s where it all falls apart.

What That Actually Cost Me (And What It Didn’t)

When I was in New Age I had a wealth consciousness framework that I just knew was going to change the world. People paid me a lot of money to walk them through it and it genuinely worked. So many of my client’s financial lives were changed. I tried to write it into a book three separate times. I ended up with chapters upon chapters of writing but no finished product.

The cost was years of thinking I could produce something valuable and watching myself fail to do it over and over. Feeling like I wasn’t disciplined enough or that I “just wasn’t a writer”.

But here’s the thing... God actually works in the most mysterious and wonderful ways. Today I’m actually so happy that work was never published. I was deep in New Age and the occult at the time. The frameworks were built on a worldview I’ve since walked away from completely. If those books had been published, I’d be spending my energy right now trying to walk them back. And even though it may have given people short-term success, I know now that the long-term consequences of favor and benefit received from the wrong Kingdom is a price that NOBODY wants to pay.

So the years of not finishing? Looking back, that was divine design. God kept those drafts in the drawer for a reason. And the experience of NOT being able to put work out, of understanding intimately why it didn’t happen, is exactly what’s helping me bring the books I’m working on now to fruition. I know the obstacle now, and I know exactly how to get around it. And this time the content is anchored in truth.

Why This Happens

Ideas come fast. Connections form constantly. I can see a framework in my head, explain it out loud to someone in ten minutes, and have them walk away understanding it completely. But the second I sit down to write it in long form, something breaks. My brain is already three ideas ahead of the paragraph I’m trying to finish. I lose patience with the slow, linear process of building an argument sentence by sentence when the whole thing already exists in my head fully formed.

I spent years thinking this was a discipline issue. That I just needed to try harder, sit longer, and force myself through the discomfort of finishing. I bought courses on writing. I tried outlines, timers, and accountability. None of it worked. The methods weren’t bad. My brain just processes information faster than the traditional writing format can keep up with, and no amount of discipline was going to change that. And I know I’m not the only one.

If This Sounds Familiar

If you’ve ever had someone tell you that you’re full of great ideas but you never follow through on them, this is probably why. The traditional tools for getting ideas out of your head and into a finished format are too slow for how your brain works.

You can talk about your business for an hour and sound like an expert. But writing a single blog post about it takes you three days and you still hate it when you’re done. You’ve started a lead magnet, a course outline, an email sequence, and a content calendar, and abandoned all of them somewhere between 40% and 80% finished. Take it from someone who has been there and has hired teams of people to make up for my perceived shortcomings. Your ideas, your brain, YOU were never the problem.

What Changed

Claude changed it.

I didn’t expect that. I came to AI for business efficiency. I wanted to speed up content production and reduce overhead. I wasn’t looking for a solution to a problem I’d been carrying since my twenties.

The first time I sat down with Claude and explained a concept the way I’d explain it to a friend, and got back a structured draft that actually captured what I was trying to say, I just stared at the screen. I sat there reading it thinking “this is what was in my head.” It wasn’t perfect. Some of the phrasing was off and the structure needed reworking. But the thinking was there. On the page. Organized. And in a format I could actually edit and finish.

I think I edited that first draft for two hours. Not because it needed two hours of work but because I kept reading it and adding to it and realizing I could KEEP GOING. The thing that always broke, the momentum, the thread, the ability to stay with a piece of writing long enough to see it through, it held. Because Claude had taken my ideas and organized them into a structured draft. My job was editing, and editing is a completely different cognitive task than creating from scratch. My brain can do that all day.

That first article was rough. I look back at it now and cringe at some of the phrasing I let through. But it was finished. Published. Out in the world. After years of nothing making it past a draft folder, I had a finished piece of content with my name on it.

51 articles later, I publish multiple times a week. The process has gotten waaaaaay faster as Claude has learned my style and my guardrails have tightened. But that first one mattered more than any of them.

This Isn’t About Laziness

The internet loves to frame AI writing assistance as laziness. “Just sit down and write.” “Real writers don’t need AI.” “If you can’t write it yourself, you don’t have anything worth saying.”

That’s all complete rubbish. And it makes me angry.

Because I spent YEARS feeling ashamed that I couldn’t do something that seemed to come naturally to other people. I watched people with half the experience and a fraction of the expertise publish books, build content libraries, and grow audiences while I sat on a pile of unfinished work and told myself I was the problem.

I wasn’t the problem. My brain processes things faster than the traditional writing format can handle, and I spent years punishing myself for that mismatch instead of finding a tool that could keep up.

Ministry leaders who teach with power on Sunday morning but can’t translate that into written content during the week know exactly what I’m talking about. Business owners who can sell in person all day but freeze when they open a blank document know too. AI closes that gap by keeping up with you.

The Tuesday Night Version

Last Tuesday night I sat down at 9pm to draft this week’s article. I was tired. In a previous life that would have meant staring at a blank page for forty-five minutes, writing three mediocre paragraphs, getting frustrated, and closing the laptop. The article would have gone unwritten and I would have spent the next day annoyed at myself.

Instead I opened Claude, told it what I wanted to say and who I was saying it for, and had a working draft in ten minutes. It needed work. Some of the sections were too polished (Claude defaults to that) and the opening didn’t sound like me. But there was something on the page. The thread existed. I spent another 30 minutes editing, pushing back on phrases that didn’t land, adding the personal details only I know, and cutting the AI patterns my guardrails are built to catch.

By 9:40 I had a finished article. On a Tuesday night. Tired. That would have been impossible for me two years ago.

Writer, Not Yet Author

I’ve always thought of myself as a writer. I write allllll the time. Long form social media posts, notebooks full of notes and ideas, and texts to friends that turn into full essays. The writing has always been constant. Finishing anything was the part that never happened.

But there’s a difference between writer and author. I won’t call myself an author until the first book is published. And for the first time in my life, I actually believe that’s going to happen.

I have a prophetic mother’s devotional book I’m working on. And a series of supernatural biblical fiction books that are a newer development but already taking shape. The difference between now and every other time I’ve tried to write a book is that I finally have a process my brain doesn’t fight. The frameworks, the guardrails, and the system I built for 51 Substack articles, all of it applies to long form.

The unfinished manuscripts from my old life are staying in the drawer. They belong there. But the new ones? Those are coming.

Who This Is Actually For

This article isn’t for everyone. If you sit down and write beautifully from a blank page, you don’t need this. Keep doing what works.

This is for the person who KNOWS they have something to say but has never been able to get it into a finished, publishable format. The person with the notebooks full of ideas. The person who can teach and talk and explain but cannot for the life of them write a blog post without wanting to throw their laptop out the window.

If you’ve been sitting on expertise, knowledge, and frameworks that never go anywhere because you can’t get past the blank page, the tool exists. You don’t have to fight your brain anymore. You just have to learn how to work with AI in a way that matches how you actually think.

51 articles. A devotional book in progress. Fiction on the way. After years of finishing nothing. You can do this too!

Everything I described in this article, the 51 articles that actually sound like me, the guardrails that catch AI patterns before they make it into a published piece, the system that taught Claude how I think and write, that’s all built on my AI Writing Guardrails. It’s the system that sits inside every Claude project I use and it’s the reason my content doesn’t read like AI wrote it. 98 patterns to avoid, voice rules, a guided builder for creating your own custom rules, and a 5-pass editing process.

Because “make it sound more human” was never going to work.

Self-Guided | Custom Build

guardrails.aiwithleah.com/info

Last Tuesday I woke up, made coffee, opened my laptop, and realized that in the last 24 hours, eleven Pinterest pins had gone live, a Substack article I wrote on Monday was already formatted for Facebook and Instagram, and my VA had flagged two things for my review with a note that said “everything else is scheduled through Friday.”

It was 8am. I hadn’t done anything yet. And the business was already running.

I have one VA who works 20 hours a week. That’s it. One person besides me. A year ago, that workload would have required a content writer, a social media manager, a graphic designer, a Pinterest strategist, and probably a project manager to keep them all talking to each other. I know because I’ve run businesses with teams that size before. I know what the overhead feels like and how much time gets eaten by communication alone.

This article is the behind the scenes of how it actually runs. Not the highlight reel version. The operational reality, including where it breaks.

What the Setup Actually Looks Like

I have three AI tools that run the core of my business. Claude for writing and content creation. Google Gemini for research and keyword discovery. Higgsfield for visual content and video. Those three do the heavy daily work. I also use HeyGen, Pomelli by Google Labs, and other tools as needs come up, but the foundation is those three. Everything else rotates in and out depending on the project.

Claude is where most of the heavy lifting happens. I draft articles, build training scripts, create email sequences, write social posts, and develop product content inside Claude. But I’m not just typing “write me a blog post” and copying what comes out. I’ve built a system around it with voice samples, project-level context that carries across sessions, and a guardrails document with 98 specific patterns Claude knows to avoid. The output is close to finished when it comes out.

Gemini handles research. When I need to validate a keyword strategy, find what people are actually searching for, or dig into a topic before I write about it, that’s where I go.

Higgsfield is the visual layer. Video content, AI avatars, graphics. The stuff that used to require either being on camera every day or hiring a designer.

My VA takes what these systems produce and moves it through the pipeline. Pinterest scheduling, content formatting across platforms, and managing the posting calendar. She handles the operational pieces that need a human but don’t need to be me, and the strategy documents I’ve built tell her exactly how to execute without guessing.

That’s the tidy version. Here’s what it actually took to get there.

What the Clean Version Leaves Out

The math IS concrete. I co-write my Substack articles with Claude. That article gets repurposed into Facebook and Instagram posts. My VA generates Pinterest pins from the core ideas. A content calendar that used to take a full team now takes me about three to five focused hours of creation time (depending on what we’re building and how many articles I’m writing that week) and my VA’s 20 hours of execution time. For real. I’ve tracked it.

But those numbers only tell you what it looks like AFTER the systems are built. They don’t tell you about the weeks I spent building a Pinterest keyword bank from scratch because nobody had done it for my niche. They don’t tell you about the guardrails document that started as a list of five things I hated about AI output and is now 98 documented patterns across eight categories. They don’t tell you I rebuilt my Claude project structure three times before it held context properly across sessions.

Every “look how efficient my AI workflow is” post on social media is showing you the after. I’m telling you the during was tedious, frustrating, and slow. I documented AI failure patterns for two years while the normal people were bingeing Netflix. That’s what built this.

The writing is where the real story is. I wasn’t writing blogs before AI. I would have never done it. My brain processes things too fast and has a hard time sitting down to focus long enough to get a long-form piece finished. I have hundreds of thousands of words written across years of work but no ability to sit down, focus, and put it all together into something publishable. Books I started and never finished. Entire frameworks mapped out in my head that never made it onto a page in any organized way.

When I realized what Claude could do, I knew I could finally start a blog. It started out slow. Claude had to learn my style, the editing process was long and tedious, and the early drafts needed heavy reworking. Now, 51 articles later, it takes no time at all. The system learned me. And I learned how to work with it. The thinking hasn’t been outsourced. The mechanical labor of getting words onto a page, formatting them for six different platforms, and scheduling them across a month? That part has (and good riddance).

Where It Breaks Down

AI still produces rubbish regularly. Even with my guardrails loaded, even with voice samples, and even with detailed context. Some days the output misses and I spend more time fixing it than I would have spent writing from scratch. (Last week Claude gave me a draft with four mirrored contrast phrases in a row. FOUR. After loading a document that specifically says never do that.)

The system works BECAUSE of the investment I made building it. Someone downloading a prompt template off the internet is not going to get these results. The “just use AI” crowd makes it sound like the tool does the work. The tool does what you’ve trained it to do. If you haven’t trained it, you get what everyone else gets.

Spiritual content, pastoral counsel, and anything that requires discernment about what God is doing in someone’s life, that’s a hard line. AI doesn’t touch it. I don’t even ask it to draft in that space. Some things require human presence and human responsibility, full stop.

And my VA still needs me. The systems reduce the number of decisions she has to make, but they don’t eliminate them entirely. She flags things, asks questions, and catches issues the system missed. The 20 hours works because she’s sharp and because the systems are well-built. If either of those factors wasn’t there, it would fall apart.

What It Costs (And What It Replaced)

I pay for Claude Pro at $100 a month, Gemini Pro at $32 a month (covers both me and my VA through our company Gmail accounts), and Higgsfield runs $150 to $250 depending on how many videos we’re producing that month. So the AI tools cost me roughly $280 to $380 a month. I’m also onboarding an autonomous AI agent right now that will hopefully help with a lot of the content production and enable us to put out even more. Getting her trained up is running $500 to $1,000 a month on its own. My VA’s compensation is on top of all of that.

Business still requires overhead in an AI economy. Anyone telling you differently isn’t doing things at scale.

But...compare that to what a content writer, social media manager, and graphic designer would cost, even part-time. We’re talking thousands per month minimum. I’ve written those checks before in previous businesses. The overhead wasn’t just financial. It was the meetings, the revisions, the miscommunication, and the management load that ate into the hours I was supposed to be spending on actual strategy.

That’s gone now. And I don’t miss it.

The Part That Actually Bothers Me

I see “I replaced my whole team with AI” content all over social media. Reels with clean desk setups and captions about making $10K a month with ChatGPT. And almost none of them are telling you what it actually took to get there.

Nobody talks about the months of building. The hours spent writing down every rule, every workflow, every pattern so that someone other than you can execute it. The failed systems you have to tear down and rebuild. The fact that AI without infrastructure produces the same mediocre content flooding every platform right now. Posting that content and calling it a business is like buying a hammer and calling yourself a contractor.

The structure took me months to build, test, break, and rebuild. I’m not complaining. I’d just rather you know what this actually requires going in than find out the hard way after you’ve wasted three months copying what some guy said worked in a 60-second reel.

What I Didn’t Expect This to Teach Me

Building AI systems forced me to look at how I ran my previous businesses. And what I saw wasn’t pretty.

I was a control freak. Everything had to be perfect, and that perfectionism slowed down my entire team from doing their jobs. I held things too tightly, revised too many times, and left so much money on the table because I couldn’t let go of the reins long enough to let other people execute. I’ve grown since then. A lot. But working with AI accelerated that growth in a way I didn’t anticipate.

Using AI requires you to loosen up. You have to accept that the first draft won’t be perfect, that the system will miss things, and that excellent is the standard now, not perfect. I’m working toward excellent every single day. And when I get on one of my perfectionist kicks and need to tear a draft apart, I don’t hurt Claude’s feelings when I tell him what he wrote is dumb. (LOL)

That might sound like a small thing. But for someone who used to agonize over every word and slow down an entire operation because of it, the ability to move fast, edit without guilt, and let good enough be good enough on Tuesday so that great can happen by Thursday? That changed how I work more than any tool ever could.

If You’re Building Something

If you’re running a small business, a side hustle alongside your full-time job, a ministry, or any kind of operation where you’re doing too many things yourself, AI can help. That part isn’t a question anymore. The real question is whether you’re willing to take the time and make the investment to build something that actually works with it.

I have capacity I haven’t had in years. Real capacity. Time with my family (we are heading to Cambodia for a four day vacation next week), time in Scripture, time to actually think about where this business is going instead of just grinding to keep it moving.

That’s what well-built AI systems buy you. Room to breathe.

The rest of this article is for paid subscribers. I’m breaking down the actual role-by-role system: what AI handles, what my VA handles, what only I can do, and the exact workflows connecting all three. If you want to see how this operates at the task level, this is it.

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