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The conversation about AI has gotten very loud. And in that conversation aspects about the human experience in the AI era get lost.

The Event Horizon is a letter built for founders who want to look clearly at what AI is actually doing—to our work, our thinking, our identity, and our businesses—and lead from that clarity rather than react from the noise.

We’re not here to chase the tools or debate the hype. We’re here to figure out what it means to stay human while building something powerful—and to do that work together, week by week.

Human lead. AI empowered. Always.

Welcome back to The Event Horizon. 🌅

Last week, this letter was about what happens when a system loses its outside reference point and starts drifting from itself, one generation at a time.

This week, I want to talk about the work required to keep yourself autonomous from the AI systems you use to build your mission-driven business. And address the phenomenon of the drift that occurs to the machines who feed on their own output actually happening to you, as well, when you lean too hard on them.

I’m going to tell you where I first recognized that same drift in myself, because it happened to me before I ever found the language for it in a research paper—maybe sharing it will help you recognize it in yourself before the distortion becomes canon.

And I want to talk about what I am referring to as The Hard Work.

I chose that term intentionally because, at this point in my life, I want to know that I can still do hard things, and this inflection point we are at with AI certainly feels like it qualifies. While the name may sound intimidating, the practices involved helped me reveal just where I was coming down on the spectrum between, “Oh, I got this; I own AI, it doesn’t own me,” and “Okay, I’m struggling to distinguish myself in the blurry line between me and the machine.” And if you are not sure where you land, it will help you do the same, finding your path to staying in the loop and in the lead.

If any of that resonates, you are in the right place.

So let’s see what it takes to do The Hard Work in the age of AI, shall we?

📡 The Signal

Four Years to Build a Brain

In London, UK, a person who wants to drive a legendary black taxi cab first has to pass a test aptly named, “The Knowledge,” one of the hardest licensing exams in the world. To pass this rigorous exam, applicants must memorize around 25,000 streets, 20,000 landmarks, and more than 320 specific routes within the Greater London area—as well as be able to demonstrate command of the route network and its elements.

The process leading up to taking the exam takes 3-4 years at minimum.

And London is not like Manhattan and its grid, or Paris and its spiral. The streets of London are windy, cobbled together, and not laid out so much as accumulated. Picture it this way: If Manhattan is a spreadsheet, and Paris is a snail shell, then London is a drawer full of old charging cords: tangled, specific, weirdly functional, and absolutely hostile to anyone who thinks they can understand it at a glance.

It’s a lot, to be sure.

Indeed, only about half of those who attempt ‘The Knowledge’ exam actually pass, so doing so is a significant accomplishment.

‘The Knowledge’ is famous in cognitive science circles, as well, because it has offered researchers an unusually vivid case of intensive, adult learning in the real world, and a behind-the-scenes look at how our brains work when they work hard, or don’t.

In a landmark 2000 study, neuroscientist Eleanor Maguire scanned 79 trainees over four years and found the brains of the ones who passed the intense Knowledge of London exam had physically grown larger, and those drivers scored significantly higher on memory tests than the ones who failed the exam—even though everyone had started out roughly equal.

Her research showed that cognitive exercise produces physical changes in the brain.

The Moment It Stopped Working

Additionally, a separate 2017 study by another team at University College London, led by Hugo Spiers, scanned the brains of 24 people while they were navigating in a simulation of driving on London streets—once from memory, and then once while following a GPS device.

What the researchers found is that participants’ brains, specifically the hippocampus (a part of the brain that helps you remember places and navigate through them) and the prefrontal cortex (which helps you think ahead and make choices), lit up at every junction along the route when they were navigating from memory. However, when the participants were following a GPS, those same regions went dark.

In other words, the brain didn’t get help from the guidance device—it wasn’t needed to make decisions, so it simply stopped participating.

That story is not about GPS, or the patchwork of London’s beautiful but complicated city streets.

It’s about what happens any time something gets handed over completely—a route through London, a first draft, a judgment call… The faculty that always made the call or execution stops firing once nothing requires it to.

Nothing to worry about, I suppose, until the day it’s needed and you find it’s out of practice, or worse, out of commission.

We’ve all heard the “use it or lose it” warning. A human’s brain is one of those muscles that needs regular cognitive exercise, in lots of different forms, to stay healthy, agile, and strong. And science is backing this up.

Here’s the takeaway: Long, demanding, real-world learning and deliberate thought exercises can alter how the adult brain is organized and how a person navigates through the world, remembers what it experiences, and solves problems it encounters.

And removing that work shuts those parts of the brain down.

The Warning That Waited Forty-Three Years

Now, here’s what complicates that tidy little lesson: the opposite instinct—be in all of the work then, to keep the brain active—fails too, just more slowly. And the evidence is older than any headline on AI.

While perusing Substack on this topic, I came across an essay entitled, “AI Brain Fry, Workslop and the Ironies of Automation” by Carl Hendrick. In it he talks about a historical paper he describes as reading more like a prophecy.

In 1983, an engineering psychologist named Lisanne Bainbridge wrote a paper called “Ironies of Automation,” arguing that the more capable an automated system becomes, the more demanding (not less) the human role inside it gets, because whatever its designer couldn’t figure out how to automate still lands on whoever’s left holding it.

Forty-three years later, a Boston Consulting Group survey of 1,488 workers proved her right in a way she couldn’t have measured in 1983: the single most mentally taxing way to work alongside AI turned out to be overseeing it, reviewing everything it produces as though vigilance itself were the responsible move.

Turns out, those workers made 39% more major errors than the ones who didn’t pursue rigorous oversight.

Which brings us back to the point of this letter, and the actual shape of the problem.

Allow the faculty to go dark, and the capacity erodes before you notice it’s gone. Keep your cognition lit up on everything, and the exhaustion produces the same erosion from the other direction.

Neither one is control. Both only feel like it, and only for a while.

🪨 First Principles

The Middle Ground Has a Name

So, there’s a version of staying in the loop that costs nothing: a glance at an output before it ships, a name attached to a decision nobody actually made. It reflects the form of oversight without the weight of it.

And a version of human oversight that costs greatly: vigilantly reviewing every output, keeping inside every system and process, which honestly, besides being exhausting, defeats the purpose of systems and processes.

The Hard Work I referred to at the top of this letter is, in my estimation, the middle ground where we keep the systems efficiencies and our agile faculties.

I see it as the effortful, countercultural choice to keep doing the parts of the thinking that are supposed to be hard, in a moment when a thousand tools are offering to make them easy. It’s the discipline of keeping your judgment—the questioning, the discernment, tacit knowledge, your curiosity, and origination—as well as accountability for the whole process and whatever you do choose to relinquish, alive in yourself and on purpose. And it’s the faith in the systems, when built with purpose and consideration that truly augment the work you are doing, while allowing your faculties a genuine break from the inevitable crush of scale.

Those faculties only get built—and maintained—by doing the work that implements them yourself. And only handing off what you define as veritably repetitive, and therefore, AI’s domain.

The reality is that the effort was never incidental to the outcome. It was how the person capable of standing behind that outcome, and behind the tools that helped produce it, got made.

Differentiating, then, is the whole discipline.

Some decisions can be handed off entirely. Some can be assisted. Some need to stay, deliberately, in human hands, because producing the answer yourself is the only way the capacity to judge that answer, and to keep control of both the tool and the person using it, stays alive in you.

And only you can decide and distinguish which is which.

It’s important to recognize, however, that being the human in that loop was never going to be one thing you do once and call finished.

The Hard Work is in opposition to all-encompassing adoption of the tools out of the seductive pull towards speed, in favor of taking a slower, more earnest approach that preserves our important faculties. It’s a personal and cultural ethic of practice: what a person must continue doing so that “human oversight” remains real rather than ceremonial.

The slower, effortful posture is not inherently virtuous simply because it is slow, though.

It matters because it keeps a person actively engaged in the activities through which judgment, learning, practical wisdom, authorship, and responsibility are developed and exercised. The risk is not that AI “makes us less human” in some literal or essentialist sense. Rather, the risk is that frictionless delegation can remove us from the practices that cultivate distinctly human capacities.

It always was, so it is not an anti-AI position I am recommending here.

Sometimes slower adoption is not failure to innovate; it can be the work required to discover whether a proposed use should exist in the first place—and how, who might be harmed, in what ways, and who will answer when it goes wrong.

And it seems large organizations are already implementing AI frameworks for risk management and operational specifications, for just such reasons.

These frameworks are often strongest in high-stakes settings—medicine, hiring, credit, public benefits, criminal justice, safety-critical systems. But I want to make the important cultural extension: even low-stakes generative AI use can shape the habits people bring to high-stakes judgment. Because a person does not develop discernment at the moment of crisis or challenge. By then they either have it or they don’t.

That kind of perspective was developed over time, through ordinary, repeated practices and ritualistic behaviors.

I have started getting the feeling, from different conversations and sources, that my idea of moderate, considered integration and taking our work with AI a lot slower—earnestly, as well as with more intention—is an unpopular one.

The hype around the use of AI and its immense potential is that it makes everything faster so why slow down and “get left behind“? ...While that’s a very simplified version of the ‘use the tech to get more efficient’ argument, I think it’s a terribly short-sighted approach to a technology we are already finding solid reasons to pull back from.

Add to that how AI systems have become so pervasive in our societal landscape so quickly (especially considering it is still so early in its own evolution), and you find that many of us already find it incredibly difficult to do anything without it being involved.

Consider this, against the story from the UCL research above: how often do you use GPS to get where you need to go now, versus how often do you allow your memory and reasoning to serve and guide your way there? Also, when you use the GPS, how much of that drive do you actually recall afterwards? …Remember, when the brain is not “needed,” it essentially shuts down.

Now lay that against the entirety of higher faculties your brain is meant to have command of.

That’s what turning over too much to AI is really affecting, and the science and research is beginning to show it. This realization is making it even more important to take back as much of the brain work as possible, in parallel to still using the technology effectively, while we still know how to do those vital parts and it isn’t completely trained out of us through the adoption of the tech and all the wondrous and concerning things it can do.

In the next section I’ll take you through my journey of adopting these tools, and where I’ve landed as a result.

Before I do, however, let’s look at my current thesis on the idea: use the tools for the repetitive tasks where your time would genuinely be better served, and stay in the process for the things that protect your creative, strategic, and evaluative irreplaceable work.

To help me with this, I have created some frameworks. I have a good one for you next week in The Event Horizon #007, so stay tuned. This week, I have an assessment to help you see where you are in this conversation.

The Hard Work Reflection Assessment

I want to share the 5-question reflection pack I created that assesses the five postures of what should be differentiated between to determine what needs you and what can (and should) be handled by the tech.

The five postures are questioning, discernment, accountability, curiosity, and origination. Additionally, there is a sixth bonus question that points to the umbrella judgment over it all: deliberate restraint.

1. Questioning — Do I know what I think, what I’m asking, or what matters to me on this topic before I ask the machine?

2. Discernment — Can I tell what’s solid here, what’s missing, and where this answer might fall short?

3. Accountability — Am I prepared to stand behind the result, explain it, change it, and fix it if necessary?

4. Curiosity — Does this answer make me want to look further, or has it made me stop thinking too soon?

5. Origination — What am I adding that the machine could not have produced without me—my experience, knowledge of the situation, values, judgment, or genuine point of view?

Then add the sixth, with the previous decisions in mind:

6. Deliberate restraint — Can I resist and choose not to take the fastest answer when the slower work of figuring it out for myself is the skill I’m building and the answer is the part I need to learn?

Together, they are the heart of “The Hard Work.”

The point of the exercise is not to find yourself in a position of anti-AI or anti-efficiency. It is to create an ‘anti-default delegation’ posture in domains where you can’t separate the outcome from the work it takes to get there, because they are constitutive of the person capable of producing and standing behind the result.

These five postures are vital to protect and maintain, as they are the capacities through which humans become responsible authors and operators: people who can form a view, give reasons for it, recognize uncertainty, learn from consequences, revise themselves, and answer to others.

None of this started as theory.

It came from watching myself become exactly the kind of AI user I swore I’d never be—a person losing ground she didn’t notice she was giving up—and then having to find my way back because I’d failed at every one of those guardrail questions. Then, in recognizing it, slowly figuring out how to take the deliberate differentiation decision back.

That’s the part worth telling next.

🫀 The Human Layer

My Journey Through The AI Land(Hell)scape

Early on, as I started down this path with generative AI tools, I realized that this technology only confirms a stance I’ve had about how we determine when something is quality, significant, and worth our attention. If something created is bad, not poorly produced, but simply crap that doesn’t move the human condition needle in any meaningful direction, we just know it.

It’s something visceral we experience, and it’s not always something we can verbally describe.

But, to borrow a line from one of my favorite writers, Aaron Sorkin, “It’s like porn. We know it when we see it.”

Early in 2023, as generative AI was making its grand entrance, I was just beginning to build my current business.

I was staunchly on the side of not applying the technology to what I viewed as my own work. Like many others, as a Creative, an artist, and a writer, at the time I saw AI as a threat to creative pursuits, and maybe even creativity itself. …I vividly remember having a conversation with my sister about whether I’d employ any of the new technology for my work. I was staunchly a “no!” …I had decided that I would never use “those tools” for the work that means so much to me.

It’s mine, and is how I identify and show up in the world.

And also… I’ve always been interested in technology.

I was certainly curious about what was developing in the AI arena, and what it might all mean.

By early 2024, it seemed obvious that the genie was out of the bottle, and there was no going back.

I started realizing that if I wanted my business (or any business I chose to build next) to have a fighting chance, any sort of viability, I needed to be embracing AI and figure how I would use it, or offer it to clients, or teach it to my audience, in ways that felt authentic and resonant—not just forced by hype and urgency.

So I started putting myself in rooms where people were actively talking about, learning about, working with, and developing all the generative AI tools coming to the forefront.

At that point, I dove in head first… And in February of 2026, right after Claude Cowork came out, I made the pivot to begin systematically handing more and more over to AI, instead of simply using a scattered, cobbled-together set of tools for the gamut of things I do.

It started as a thought exercise of sorts, a personal experiment to begin to surrender control (writers and artists will recognize this), hand the tech my workflows, and in the process help me define what this meant for me specifically. It was important to me to understand my thresholds before I started advising others on what “good” looks like, and what I found the pitfalls to be as they reveal themselves to me.

Boy howdy, did I find the pitfalls!

Pursuing this path of AI has both broadened my perception of what’s possible—and even who and what I am—and helped me to recognize that fundamental truths of being human are precisely that for a reason, no matter the era, arena, or age.

Most recently, I have come to realize that the answer is definitely going to be different for every individual (let alone organization) regarding how much you use the tools, for what, how long, and at what cost (the very least of which is monetary).

As for me, the first half of this year—while I was helping to take care of my last remaining elder relative, committed to a couple of initiatives meant to help move my business move forward, and working on a timeline of milestones I had set in motion before the new year—I found that I was both super overwhelmed and exhausted.

As a result, I found myself leaning on my AI assistants and systems more and more to get things done and off my plate.

Something I started to see in myself was the fact that as I did, two things became very apparent… I didn’t like the work I was producing nearly as much, and I also didn’t enjoy the process of doing the work at the same level, either. For someone who identifies as a writer, that was a real gut punch.

Now obviously, taking the circumstances into consideration, I was tired and stressed, and it could have been me seeing that in myself and what was coming out of me.

And that was certainly a factor; I don’t pretend otherwise.

But I’ve been paying attention, almost from the jump, because the aspect of how using these tools actually changes and affects a person has been one of the things I’ve been most curious about from the beginning.

When I thought I was recognizing changes in the way I create and think, I started deliberately taking things back from the technology till I could sort it out.

Now, I write the majority of my pieces myself again, using the tools for research, think-through exercises, outlines, as well as a review after I’ve nailed down the issue, that kind of thing.

I have also taken other steps to ensure I maintain my craft, my creativity, and my capacities. I will go deeper into those practices in the next issue of The Event Horizon.

My single biggest take away so far, and what I keep coming back to from almost every angle, and in so many conversations I’m having, is this: right now—not in a year from now, not 6 months from now, not even 3 months from now—is when people need to begin deciding in earnest how much they stay in the loop when using these tools, and at what stages of the process.

Everything I’ve been experiencing keeps coming back to the judgment card.

No matter the tool, task, or tolerance level I am working through, how I show up and actively insert my mind into the equation—my thoughts, my insights, my discernment, my values, my bullshit detector, my curiosity, my intuition—and whether or not I use my brain to add to the process rather or surrender it to the process, determines the quality of output I get.

And every single time I insert my brain into the process, I get infinitely better output.

One more element I am beginning to recognize makes a difference in this conversation… Where you are starting when you begin using the tools.

I had been building my niche and writing around my topics for a while before I inserted AI into my work. So, I had a library of content to train my AI systems on, as well as what good looks like for me. Without those elements, I don’t know that I’d personally been comfortable enough to dive in head first, nor would I have had the discernment to know as quickly as I did when I had relinquished too much or seen how the work was beginning to fail.

I believe that people who are just beginning to build a repository of work, while simultaneously adopting the tools to assist them, will likely be the ones most susceptible to the homogenization the AI models outputs lean toward.

So, here’s where I have landed so far: for all of the “ease” promised by AI, the necessary hard parts before you jump to the ease is what makes the difference. It’s what makes the output worth energy or attention.

The “hard” is what makes it good. And it always has been. AI has simply shortened the curve of recognizing it.

The detrimental part is not the tool.

The detrimental part is using the tool before building the inner reference point—the five postures that keep your higher faculties in play—that the tool is supposed to serve.

🌅 Light on the Horizon

The Good Version, Already Happening

Assistive AI has moved well past the hype cycle this year.

OrCam MyEye, dot’s Lumen glasses, and Microsoft’s Seeing AI are now translating real-world scenes into real-time audio for blind and low-vision users, joined at CES 2026 by self-driving mobility chairs and neural earbuds built for the same purpose.

This is AI doing something a human cannot do unaided: reading a room, a street, a face, in real time, so a person can move through the world with more agency. It’s the differentiation argument working exactly as intended: the tool doing what only the tool can do, the human staying exactly where judgment is needed.

Source

Insilico Medicine’s Rentosertib just became the first AI-discovered drug to reach Phase III trials.

It’s entering a 320-patient study across China to treat idiopathic pulmonary fibrosis, a progressive, scarring lung disease. The molecule came out of Insilico’s own Pharma.AI platform: PandaOmics for identifying biological targets,

Chemistry42 for generating and screening candidate compounds, doing the genuinely mundane-at-scale work of examining thousands of molecular possibilities so human researchers could spend their judgment on what only they can do: designing the trial, caring for the patients enrolled in it, and interpreting what the results actually mean for someone living with this disease.

If it holds up through Phase III, it becomes a treatment reaching people who are currently waiting on one.

That’s the differentiation argument, playing out at institutional scale.

Source

⚠️ Concern Worth Naming:

The Cost Hidden Inside Convenience

Something worth watching:

MIT Media Lab researchers gave this pattern a name: cognitive debt.

Their study of writers using ChatGPT found progressively weaker brain connectivity the more the tool did the thinking, and when those same writers tried working without it afterward, their neural engagement stayed suppressed.

The debt doesn’t announce itself. It accumulates in the background, and every small convenience delegation makes the next one easier to justify, while the underlying capacity gets a little harder to reach when you actually need it.

The good version of this looks like:

Resembling the two items above, the good version of this looks like AI doing what only AI can do, or what’s genuinely mundane at scale, while judgment stays exactly where it belongs.

What separates the two is whether the decision to hand something off was actually a decision, made on purpose, rather than just the path of least resistance.

That differentiation is the loop. Stay in it.

🎯 Your Move

This week, notice the moment right before you hand something off.

Pick one task you’d normally let the tool take without a second thought, and pause there instead.

What would you actually build if you stayed and did this one yourself?

Then choose, on purpose, knowing exactly what you’re trading and why. That’s the whole practice: staying the one who decides, every single time.

📡 Signal Back

This week’s question:

Is there something you swore you’d never hand over to AI that you’ve since handed over completely?

What changed, and was it actually a decision, or did it just happen to you?

Help me see how this is affecting other founders. I read every reply.

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply.

Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread (this issue is proof of that).

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

🌿 The Understory

What you just read is the argument. What follows is the work.

What comes next was built for The Understory, and it picks up exactly where the argument left off: from recognizing the drift in theory to finding it in the places you actually work, and deciding on purpose where the line between what you hand off and what you hold belongs.

This week’s Understory is two things. An Integration Frame—the postures from the Hard Work Reflection Assessment carried into four places you’re already working: your content, your voice, your client relationships, and your systems, with the posture that does the most work in each one. And a Research Deep Dive—the annotated source set behind this issue, from Maguire’s taxi drivers and Spiers’ GPS study to Bainbridge’s 1983 paper and this year’s cognitive debt research, for the writers, coaches, and expert-to-educators in The🌳Grove who want the receipts underneath the argument before they carry it anywhere else.

And at the close—an honest note on how I used AI this week. This is a standing section in every The Event Horizon Understory, because my position on AI is simple and steady, and now refined: use the tools, stay in the loop, differentiate the threshold and deliberately restrain when needed—and stay in the lead. These are not in conflict. The Understory is built on exactly that premise.

Let’s get to work.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →

✨ Here’s to the hard work of noticing before the drift becomes the default.

Stay curious. Stay human.

~StacyLynn
Founder, Sitting Pretty Strategies
Root Deeply. 🌳 Rise Differently.

P.S.

One more thing: I mentioned above that I used frameworks to help me protect my craft once I noticed the drift. These have become real practices I use regularly, alongside the recognition I gained from the assessment I shared in this week’s issue.

In next week’s letter, I’ll share the arc and the practice framework that has helped me come back to my work and creative identity.

And if you know a founder who’s caught themselves breaking their own “I’d never” rule about AI, and can’t quite pinpoint when it happened, forward this their way. 💌

The conversation about AI has gotten very loud. So far, in that conversation aspects about the human experience in the AI era have gotten a bit lost.

The Event Horizon is a Thursday letter built for founders who want to look clearly at what AI is actually doing—to our work, our thinking, our identity, and our businesses—and lead from that clarity rather than react from the noise.

We’re not here to chase the tools or debate the hype. We’re here to figure out what it means to stay human while building something powerful—and to do that work together, week by week.

Human lead. AI empowered. Always.

Welcome back to The Event Horizon. 🌅

Two issues ago, I told you to hang onto a line: the training data is us.

This week, that line runs out. AI is already running out of human-written material to learn from—and the fix the industry has already started reaching for is AI teaching itself off its own homework.

AI teaching itself off its own homework is a bit like a rumor citing itself as a source—or spellcheck learning spelling from your typos folder. Either way, the system isn’t being corrected by reality. It’s being trained by its own remnant.

There is a huge debate raging around the how, who, why, and what LLM’s should or shouldn’t train on. It is, without hyperbole, an extremely nuanced and complex conversation, to be sure.

What I’d like to talk about today is the actual path in front of us right now, possible forks in the road, and a question at the end—instead of an offered solution.

Because something that involves the majority of humanity is not something we solve in an essay.

That only happens with continuing the conversation, and adding ideas to the collective brain until each expression of the argument is not only addressed but also reasoned out and considered.

This letter is merely one open path to continue that conversation.

📡 The Signal

Twenty Generations From the Original

What do twenty generations, seven photocopy machines, four distinct signatures, three density settings, and one forensic document examiner have in common?

No, this isn’t a “walked into a bar” joke, and it’s not some bizarre “12 Days of a techno Christmas” song, either.

In 2017, a forensic document examiner by the name of Robert Gervais ran a rigorous study on photocopier distortion. He took four genuine signatures—he wrote two each with two different pens, and at two different speeds with each pen, giving him the original four samples to work from. Then he ran those originals through a collection of photocopying machinery of varying capabilities and at different setting capacities. Some were analog, some were digital, and different makes and models, testing different levels of technology—seven in all.

For each original handwriting sample, he then reproduced twenty generations—in other words, using the previous copy to make the next copy generation. In total, he produced 1680 images for this study.

Here’s what he found: the first four generations of any of the series of copies didn’t register obvious distortions to the naked eye, even through the software the series was uploaded into to examine the images. They looked clean, and mostly indistinguishable from the original, even on machines that go on to produce severe distortion.

By generation twenty, however, some are veritably unrecognizable.

The damage doesn’t announce itself early. It compounds quietly, underneath a surface that still looks trustworthy—and by the time it’s visibly wrong, you’re many generations past being able to point to where it started.

Here’s why I’m telling you about a paper photocopier study in a newsletter about artificial intelligence.

Large language models (LLMs) are continually trained on data. That part isn’t news to anyone reading this. What’s newer is what that data is increasingly made of.

For years, the raw material was us—the accumulated, uneven, occasionally brilliant mess of everything humans have written, photographed, recorded, and put online. The original signature, so to speak. But AI-generated text, images, and audio have been flooding back into the same pool they were scraped from in the first place. Which means the next generation of models isn’t only learning from the original anymore. It’s learning from copies of copies—its own prior output, filtered back in as though it were fresh material.

Same mechanism. Different medium.

A model trained partly on a previous model’s output is generation two. Trained on a model trained on that output, generation three. And so on.

The photocopier never cared whether it was reproducing a signature or a sentence. Neither does the AI model.

And the mechanism itself isn’t new, either. It existed before AI came into full view. AI didn’t invent generational distortion. It just found a way to run it a lot faster, on a lot more material, with a lot less time between generations to notice.

The Well Is Already Running Dry

So, if the technology is still so new, why does AI even need to train on its own data?

Let’s start with the research from Epoch AI that estimates available new high-quality public human text runs out somewhere 2026 to 2032. You read that right: the low end of the window is now.

How did they arrive at that timeframe?

As the authors begin in their report, “Scaling has been a key factor driving progress in AI. Models are growing in parameters and being trained on increasingly enormous datasets, leading to exponential growth in training compute, and dramatic increases in performance. For example, five years and four orders of magnitude of compute separate the barely coherent GPT-2 from the powerful GPT-4.”

Basically, every time the model evolves into its next iteration of capability, its appetite for new datasets increases.

Researchers who track this for a living estimate a point inside the above window as when the usable stock of human-written material online gets fully used up in past training datasets, at the pace things are currently going.

But the pace isn’t a fixed variable, either.

Some companies are already feeding models data faster than it naturally accumulates, just to squeeze out more performance sooner.

So, here’s the number underneath the window: pushing that hard, the target inside the window collapses down to 2027. Push harder still, with more aggressive scaling strategies, and it collapses again.

And according to the Communications of the ACM, that target date isn’t actually somewhere off in the even near future… It’s already here.

The CACM piece earlier this year, “Model Collapse Is Already Happening, We Just Pretend It Isn’t,“ confirmed collapse is measurable in production systems now. Through background-removal software failing on specific hair textures, image generators drifting toward homogeneous output, they show that this is not a future risk anyone can still treat as theoretical.

Sound familiar? That background-removal glitch, the subtle drift toward sameness in image generators—that’s generation four. Recognizable. Still trustworthy-looking. Which was exactly the problem with the signature, too.

By the way, Alex Williams, the author of the CACM article, likes my photocopy study for this, too (though he doesn’t know it). He uses the metaphor, as well, for what model collapse looks like.

Model collapse isn’t a future risk anymore, and the fix everyone’s reaching for is what’s causing it.

Downstream From the Well

Generation twenty isn’t the only problem. Nobody’s even agreed yet on what generation one was even allowed to be copied from.

While one clock is running forward, toward a shortage nobody’s solved yet, a second clock has been running backward this whole time. Nobody’s actually settled how the current generation of models got built in the first place.

2026 denotes three years since the separate reckoning of the second clock started over the data already used to build the current generation of models through (still) active lawsuits. The unsettled legal fight between The New York Times v. OpenAI and Microsoft over copyright infringement doesn’t look any closer to resolution.

And Anthropic is in the ring, too, with an actual “after the fact” lesson on what the price of consent really is. At the start of building LLMs, companies were using books to train the language models, before moving on to the whole of the internet.

In June 2025, the judge for the case a class of authors brought against Anthropic for copyright infringement actually split the verdict. He ruled that the training itself was legal, but where the books came from and what Anthropic kept was not. The court ruled a settlement of $1.5B be paid by Anthropic for almost 500,000 works it used to train Claude on.

The industry is still settling the past whilst mortgaging the future in the same news cycle. The industry calls it a data shortage. What’s disappearing is harder to license back than a database.

If the well is running dry, and what’s refilling it is increasingly the machine’s own reflection, what happens to the next model that trains on it, and to the humans reading, writing, and thinking inside the same polluted supply?

🪨 First Principles

Every Machine Distorts Differently

We have now come to the fork in the road… or rather what I currently see as four forks in the road. Four possible paths through this training data challenge in front of us.

Paying For What Was Already Taken: Fork one, retroactive licensing

In this scenario, we collectively choose to settle for what was taken, and the models keep training on scraped material, only under new terms.

Like the authors in the Bartz v. Anthropic case above, compensation would mostly come retroactively on the material these LLMs have already been trained on, with the knowledge that the compendium of fresh human-written text is dwindling to a pace that cannot keep up with the pace of the models expansion and training need.

Consent becomes an “after the fact” settlement, not a starting condition.

Ask Before Taking: Fork two, consent-first sourcing

Last fall, Warner Music Group (WMG) partnered with Suno, an AI music generator, striking what they’ve called, “a first-of-its-kind partnership.”

In the Warner–Suno’s model, AI systems are built only on material artists knowingly gave consent to be used, with ongoing control retained.

WMG’s CEO Robert Kyncl said of the development, “AI becomes pro-artist when it adheres to our principles: committing to licensed models, reflecting the value of music on and off platform, and providing artists and songwriters with an opt-in for the use of their name, image, likeness, voice and compositions in new AI songs.”

So, as this model demonstrates, this scenario is slower, and a bit smaller, but real consent—instead of retrofitted consent.

Betting The Mirror Won’t Crack: Fork three, synthetic data at scale

Following the path fork number three lays out, we collectively accept the shortage. We also accept the degradation risk that we talked about in The Signal, which, as you remember, is already occurring. The idea would be to optimize around it as much as possible.

At the moment, “optimizing datasets” is the main alternative lever to raw scaling. Instead of asking “how much bigger, how much more,“ it starts asking “what exactly are we feeding it, in what order, and from where.“

What would that look like in real life?

Well, Microsoft has already “optimized” this process, deliberately, and it worked better than they anticipated. They built a model called Phi-4 almost entirely on data it generated itself rather than scraped from the internet. Roughly 50 different types of made-up training material, built specifically to teach the model different skills.

And it outperformed larger models that were trained the traditional way, on real, human-written material.

This is the clearest real-world proof that Fork 3 isn’t just a risk, it’s a strategy some companies have already bet on and won with, at least so far.

Anthropic’s actually been doing a version of this since 2022.

Their models help train themselves by critiquing and revising their own answers, and those self-critiques become part of what teaches the next version what “good” looks like. It’s older than the current shortage conversation, but it’s a clean example of a company choosing, on purpose, to let a system learn from itself rather than treating that as an accident to avoid.

This isn’t hypothetical, as it is increasingly the answer companies who build and train LLMs are reaching for. So while it is a viable answer to the well drying up, it is also the biggest culprit to the echo beginning to happen in the output of the models.

A hall of mirrors, producing quality drift nobody chose, absorbed without fuss, fight, or force, generation over generation.

The Door Nobody’s Built Yet: Fork four, collective infrastructure not yet built

While there isn’t a lot of building in this area yet, there is quite a bit of research, policy work, and even one viable example. So let’s take a look…

The whole idea already has a name, and someone gave it one three years before this became an emergency.

In 2023, researchers formally proposed a “public data trust,” a mechanism to hold and license AI training data on behalf of the public, back when the shortage was still a forecast, not a headline.

They wrote up the blueprint for doing this at real scale. The academic paper proposed an organization that would hold and manage training data on behalf of the people who created it, so individuals aren’t negotiating one-on-one against companies with far more power and lawyers than they have.

A separate peer-reviewed study asked the harder question: would this actually work?

Author Pamela Samuelson didn’t ask “is this a nice idea?“ Instead, she look head-on at the more real-world challenge of “is collective licensing genuinely feasible at the scale AI training actually happens?“

So, serious researchers are testing the idea against reality, not just proposing it as a lovely premise.

Even with the proposed challenges, a government is already trying to build this, not simply discuss it.

In March of this year, the European Parliament adopted an actual system for this idea. A legal framework to clarify licensing rules for generative AI and support voluntary collective licensing agreements between companies training AI on copyrighted creative work and creative industries. The EUIPO, the EU’s intellectual property office, would facilitate, as a trusted intermediary, management of opt-out registries, and help broker licensing deals. It also explicitly calls on the European Commission to look at fair compensation for past uses, not just future ones.

Real institutional movement towards the fourth fork’s path, not just theory.

And as it happens, someone already built a small, working version of this.

Fifteen choirs in the UK got together and set up their own system for deciding, collectively, whether—and how—their recorded singing could be used to train an AI voice model. This is not a company deciding for them, or a lawsuit after-the-fact in play. This is the choirs themselves, governing the terms up front, together.

It’s small, and it’s only one narrow use case, but it’s real and it’s already been built.

So, data cooperatives, revenue-sharing models, and policies that could move it all forward, are being theorized on standards that don’t exist—yet. So far, it costs nothing but time and imagination, because it just isn’t real at any scale yet.

Ultimately, there’s no way to definitely say how this whole thing sorts out.

One thing is clear, however: this isn’t a future choice.

It’s a current big, hairy mess of overlapping ones, happening simultaneously across different companies, lawsuits, organizations, and platforms.

What’s genuinely useful to a conscious founder isn’t a tidy wrap-up of which path wins. As I said, there is no way to really know that yet. What’s important for you is seeing plainly that several paths are being taken at once, by different actors, with no collective agreement, and understanding what each one has costs and to whom.

🫀 The Human Layer

Are You Still Generation One?

In issue #003 of The Event Horizon, we talked about AI detectors and how turning over judgement to them begins the slippery slope to the flattened average of all human writing.

As whole, my argument across Rooted & Rising on originality is that it is a personal and brand differentiator. And it certainly is.

There’s another dimension, however, to the importance of originality that has surfaced through this topic of AI data and output distortion. As a contribution to a shared, finite resource—AI training data—what “average” will mean once the majority of output is built from AI’s own yield rather than human writing will expand exponentially. You’ll see it in the voice homogenization even for the output of human writers, too, as they increasingly lean on AI systems in their production processes.

So it won’t just be the models who are bland, lifeless, and without nuance.

If we are not careful, conscientious, and agile, it will happen to our voices, too, as a never-ending ouroboros of recycled thoughts, interpretations, and evaluations become the well everyone fills their metaphorical chalices from. As we choose to continue to write alongside the same handful of tools—without replenishing the foundation we all pull from, and without being more deliberate in how much of what we produce is our own original thought and examination—it will become harder to locate distinctiveness as everyone’s influences begin to overlap, dissolving into the gray blur of homogenization.

All while that very distinctiveness becomes more and more valuable.

This naturally leads to a discomfort of realizing your own authored thinking is now, in a very literal sense, scarce raw material, not just a creative expression.

Sitting with a question that doesn’t resolve itself is the work right now—at least it is for me.

And I think rather than reaching for false closure, the discomfort—the struggle of this big idea and complex problem—is the important thing in the long run. It raises more questions than answers, and I think it should.

Questions like:

Now that the average itself is starting to fill with AI’s own blended output, while human writing simultaneously drifts toward assistant-influenced patterns, does that make original work truly more valuable, or more exposed? Or both?

Will I notice my own drift by generation four, or only once it has already spanned out to generation twenty?

How do we ensure that these systems both stay in service to humanity and stay equitable?

Which fork in the road brings humanity to the best possible future, or is it some collaborative paths of multiple forked paths?

🌅 Light on the Horizon

Where the Signature Still Holds

A Catalogue of What’s Still Clean

Someone is keeping score of what’s still generation one, in public, for free.

Low-background Steel is a public catalogue, maintained by hand, of data sources whose contents predate or deliberately exclude the flood of machine-generated text, images, and audio that started in late 2022. Not a company, not a lab. A list: a Common Crawl snapshot from August 2022, a Wikipedia dump from the same month, Project Gutenberg, a database of word frequencies that stopped updating on purpose once its sources got contaminated, an archive of ship videos, a decommissioned coal mine in Svalbard holding a 2020 snapshot of every public GitHub repository.

The name borrows from actual low-background steel, metal forged before 1945 nuclear testing, uncontaminated by fallout, still used today in instruments sensitive enough to need it. Someone looked at the exact problem this issue has been circling and reached for the same idea: catalogue what’s provably clean, before it’s impossible to tell anymore.

It’s the plainest possible answer to “how would you even know which generation something is.” You’d have to have kept track from before the copying started. Someone already did, and it’s updated as recently as today. Read more →

Building the Thing That Tells You Which Generation This Is

If the real fix for model collapse is “taking data provenance seriously as an engineering discipline,“ someone’s already building that infrastructure, and it’s not staying theoretical.

Content Credentials, built on a standard called C2PA, attach a verifiable record to a piece of media: what made it, what it’s made from, whether it’s been touched since. Google’s Pixel 10 and Sony’s newest professional video camera both build this in at the moment of capture. OpenAI embeds it in DALL-E 3’s output. The EU’s AI Act and a California law now require exactly this kind of disclosure, and the standard is what makes it enforceable rather than a policy with no teeth.

Not perfect, and certainly not universal yet.

But it’s a real, working answer to the question this R&R edition has talked about at length: not “did AI make this,” but “can you actually trace where this came from.” It’s not a proposal. It’s already in a phone millions of people own. Read more →

⚠️ Concern Worth Naming:

Model collapse, at founder scale.

Something worth watching:

The CACM finding above names an industry-level risk.

The founder-scale version is every deferred-origination decision—letting AI draft something end-to-end rather than originating and refining as you build with it—is a small contribution to the same averaging mechanism, at the scale of one person’s output instead of one model’s training run.

Run that across enough founders leaning on the same handful of tools, and the risk stops being personal.

The good version of this looks like:

Founders who keep originating aren’t just protecting their own differentiation. They’re keeping the well from narrowing for everyone drawing from it next, human and machine alike.

That origination is the loop. Stay in it.

🎯 Your Move

You just read four forks. None of them got my verdict, on purpose.

This isn’t a “which one’s right?“ kind of essay. It’s the “here’s where we actually stand“ kind.

But I’m not going to ask you to name your fork without naming mine.

So here’s mine: Fork number four is the one I personally lean into most as what I believe will be the best path forward, both for humans and for the models. And with a working framework, a specimen to examine, and institutional movement in the arena, the future on the path that follows fork number four looks bright indeed. 😎

I could very well be wrong about that in a year. Less, even. Genuinely.

There could be forks in the road that haven’t shown themselves yet. This is moving fast enough that I’m holding it all quite loosely, on purpose, while still keeping the questions top of mind.

SO, your move this week isn’t a task.

It’s this: which fork do you find yourself standing closest to right now, and why.

Not the one that sounds best. The one that’s actually true of how you’re operating.

Then tell me, down in Signal Back. That’s not a formality this week. That’s the actual point of writing this letter, that ends with questions, instead of an essay that ends with a tidy little bow on it.

🧭 Constellation Compass

🌌 Elsewhere in the SPS ecosystem recently:

🗺️ In this issue of Elegant Email Ecosystems, This week I made the case for framework thinking: why expertise without a container survives the moment it’s delivered and disappears the moment someone tries to pass it on, and how a signature framework turns scattered depth into something a client can actually carry forward. [Read E3 #030 →]

🌱 In this issue of The Master Work, I sat with the two blueprints every business runs on, the one on paper, and the one made of everything you haven’t examined yet, and what it actually takes to catch a fear or an old story before it makes a decision you’ll mistake for strategy. [Read TMW #003 →]

🔮 Inbox Alchemy is the lab side of SPS—where ideas like these get turned into practice through a sequential, evergreen email experience—the hands-on companion to everything SPS publishes, one issue at a time. If you’re ready to move from insight to implementation, [Join us in the lab →]

📡 Signal Back

This week’s question:

Where do you land on the forks above?

I’m not looking for a polished take, just where you actually are right now.

I read every reply, and this issue especially will help me see the whole picture if you signal back.

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply.

Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread (this issue is proof of that).

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

🌿 The Understory

What you just read is the argument. What follows is the work.

What comes next was built for The Understory, and it goes where this issue has been pointing all along: into the actual practice of staying part of what’s worth drawing from, instead of only ever drawing from it.

This week’s Understory is three things. A Practice Protocol: “The Contribution Ledger,” a simple, ongoing habit of tracking what you originated this week versus what you recirculated, continuing the ledger this letter’s been building since #003’s Rejection Ledger and #004’s Narrate the Call. A closer look at Fork Four, and why, out of four honestly-argued paths, it’s the one I’m personally betting on, the fuller case I didn’t make in the letter above, on purpose. And a Research Deep Dive: the full annotated source set behind this issue’s argument, built for the educators, coaches, and expert-to-educators in the Grove who’ll want the receipts.

And at the close—a brief, honest note on how I used AI this week. This is a standing section in every The Event Horizon Understory, because my position on AI is simple and steady, and now refined: use the tools, stay in the loop, notice the pattern—stay in the lead. These are not in conflict. The Understory is built on exactly that premise.

Let’s get to work.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →

✨ Here’s to staying part of what’s worth drawing from, not just what draws from us.

Stay curious. Stay human.

~StacyLynn
Founder, Sitting Pretty Strategies
Root Deeply. 🌳 Rise Differently.

P.S.

If you know a writer, researcher, or founder who’s been quietly producing real, original work without realizing how much that’s actually worth right now, not just to their business, but to the well everyone’s drawing from, forward this their way. 💌

The Master Work is a Sunday letter for founders who have always known: the wider you draw from life, the deeper you can build. The muse of inspiration—and the source of wisdom—comes from the menageries of Life. The songs, the stories, the essays, the experiences, the conversations, the contemplation—all of it feeds the mission. Everything you’re reading, hearing, noticing, and living belongs here, in The Master Work.

Welcome back to The Master Work!🌳 Thanks for sharing a little of your Sunday with me.

Every business ever built runs on two blueprints at once. One is the one you can point to—the offer, the pricing, the systems, the plan. The other is much more subtle, and it’s made of everything you haven’t necessarily looked at yet: what you believe about worth, what you’re afraid of losing, what you’re still trying to prove, and to whom.

This week’s The Master Work issue is about that second blueprint—the one that quietly determines how well the first one actually works.

I’ve been sitting with a version of this question for a while now: how much of what I call “the business” is really strategy, and how much of it is my nervous system, my ego, and/or an old story making decisions I’ve mistaken for sound ones?

The experience of recognizing one such pattern across my life arrived through a conversation with a loved one this week, suddenly highlighted for me in neon colors, in a situation that had nothing to do with SPS or Rooted & Rising at all—which is usually how you know a pattern is real, and not just a business problem wearing a philosophical costume.

In real time, I was watching myself do the thing I always do, and finally catching it while it was actually happening, instead of noticing it some time later the way I usually do. And I also saw in it the bleedthrough to how I can sometimes operate in my business, as well, as a result.

It got me thinking how our inner life and our outer experiences cross over so much more than we realize in our day-to-day living and building.

So that’s what we’re doing here: looking at the human underneath the founder, and what it actually takes to tell the difference between what the business needs and what something older in you is asking for instead.

And the question we’re asking this week is this…

What is actually happening inside me—and how is it shaping the business I am building?

🌱 The Seed

A mission-driven business is never built from strategy alone. It’s also built from the founder’s beliefs, fears, values, emotional patterns, ambitions, blind spots, and unresolved stories.

Self-awareness is what lets you tell the difference between what the business genuinely needs and what your nervous system, ego, or conditioning is demanding in the moment.

Why does this matter for you and your business?

Conscious founders often assume intention is enough protection here—a sort of compartmentalization between worlds.

It isn’t, however, precisely because unconscious patterns arrive wearing the costume of good instincts: a fear of rejection looks like humility, a need for approval appears to be flexibility, and an old story comes across as merely a strong opinion.

You cannot build an ethical, values-aligned company while remaining unaware of the internal forces shaping your choices.

Your intentions may be sincere, but intention alone does not prevent unconscious patterns from entering your marketing, your leadership, pricing, client relationships, or the company culture.

Because the real question was never whether a founder has blind spots.

It’s whether those blind spots get to run the business, or whether you catch them in time to choose deliberately.

📖 The Work

The saying “as above, so below” is fitting here—or the more accurate version that comes from its kissing cousin: “as within, so without.” While the saying is one of those phrases that gets used in mystical, spiritual, self-help, and philosophical references, its most appropriate context for our conversation is its psychological one.

First, a little back story.

The saying, “as above, so below” comes from Hermetic tradition, and the idea is that patterns at one level of reality may reflect at another.

The phrase is modern shorthand for a line on the Emerald Tablet—a short text of the Hermetic/alchemical traditions that first appeared in Arabic sources from roughly the late eighth or ninth century.

So it’s been around awhile.

The early text has been twisted and molded and recombined, as tends to happen as language and wisdom evolves and moves through time. The original Arabic version, however, actually mirrors the phenomenon I want to talk about today… The thing I want you to notice both in your business and in yourself:

“What is above is from what is below, and what is below is from above.”

This version emphasizes the presentation I am using, which is reciprocal generation.

In other words, what happens inside us affects—and is often reflected in—what happens outside of us, and vice versa. So, rather than different areas of our lives being separate or compartmentalized, as we often portray to ourselves, everything is connected and relational.

And the version I lean into, “as within, so without,” is an apt reminder that what we’re experiencing internally can influence what you create externally.

Which is how we arrive at this week’s The Master Work issue.

You see, I would argue that those two things—your inner world and your outer life—have always been inextricably linked. Which is to say, patterns echo across levels of a system—in a society, in a business, in a body, in the experience of a human being—and examining one seemingly unrelated level can help you garner understanding of another.

This is the territory of Self Awareness.

And the phrase above does a particular kind of ask of your attention… When you notice a pattern somewhere, it behooves you to look for where else that same pattern might be expressing itself.

Sometimes what looks like five separate problems or challenges is one pattern wearing five different coats, holding the same umbrella.

If the metaphysics is a bit much for your sensibilities, taken as heuristic, you can think of it this way: “What recurring structure am I seeing across scales?”

We are not isolated observers of reality, but rather participants in nested systems—biologic, social, ecological, and symbolic.

In the arena of self-awareness, “as within, so without” can help you turn attention towards how inner habits and behaviors can shape what we then notice externally, what we communicate, and what we create or build.

Okay, back to what’s actually happening inside a conscious founder—and how it is shaping the business being built.

So, what is self awareness?

To start with, let’s acknowledge that it isn’t just one thing.

Self-awareness is actually a cluster of capabilities that sit at the intersection of our psychology and philosophical alignment, neuroscience, ethics, and the social aspect of our experience.

This cluster can develop unevenly—across areas like identifying what you feel, noticing your thoughts and beliefs, seeing what motivates you-and how, recognizing patterns that repeat in your behavior, having some awareness of how other people perceive you, and honesty about the story you center your life around. Often several layers are operating at the same time.

The optimal operation of self-awareness isn’t in constant or relentless self-monitoring, though.

It’s actually more about creating more connection with your reality and giving you more choices in your response to your experience of it.

Healthy self-awareness pulls information from two sources before evaluating an experience.

First, from the first-person perspective: what you actually experience—feelings, memories, bodily signals, thoughts, intentions.

Second, a third-person perspective gives the evaluation depth and nuance that no one person can have living inside their own internal bubble. So this includes taking into account the results from repeated actions, what others you trust observe, and what patterns appear across multiple situations.

Together these two perspectives offer a much more complete picture of your reality.

You cannot change a founder pattern you cannot see.

Before changing your pricing, strategy, schedule, your offer, or team, you need to understand what is driving the current behavior. A surface-level business problem may have a practical cause, an emotional cause, or both.

Self-awareness helps you address the correct problem.

A thought such as No one will pay for this, I am behind, or I need to act now is not automatically a fact.

Founders make expensive decisions when temporary thoughts are treated as objective business intelligence. I know in my entrepreneurial journey, I have succumbed to this trap on more than one occasion.

You can acknowledge a thought without building a strategy around it.

The more precisely you can identify what you are experiencing, the less likely you are to act it out unconsciously.

“I feel bad about this launch” gives you little useful information.

“I feel disappointed by the result, embarrassed about being visible, and afraid the market response reflects my worth” gives you something real to examine.

Precision of language creates room for discernment.

I have spent most of my life believing my need to help whenever I can is one of my best qualities. This year, however, inside a situation with a family member, that same instinct cost me something real, that I finally noticed in real time: my health, my bandwidth, my peace.

This month, I had to sit with an uncomfortable question out of that noticing—what happens when the thing you’d call a strength is actually running the show without your permission?

Self-awareness is not an invitation to judge yourself more efficiently—or harshly.

You are trying to observe your patterns clearly enough to work with them—not gather evidence against yourself.

A conscious founder needs the capacity to say:

This pattern is affecting my leadership, and I can address it without treating myself as defective.

Accountability and compassion are not opposites. You need both.

When I named the pattern I saw that had actually been repeating across decades, rather than castigating myself for missing the signs and attributing obvious consequences in places they didn’t belong,

I had to take responsibility for the behavior that fed the pattern, yes.

But the more useful response was to take this “new” information, and make better, more conscious choices from it. And I have already begun to.

Entrepreneurship places pressure on identity, security, visibility, control, belonging, and self-worth. It often magnifies patterns that are easier to avoid in less exposed environments.

The business did not necessarily create the pattern. It may simply be revealing it.

That revelation can become useful information rather than a verdict.

Without self-awareness, founders often:

• Build offers around what earns approval rather than what creates meaningful value.

• Say yes to misaligned clients because scarcity feels louder than discernment.

• Confuse visibility discomfort with strategic misalignment.

• Interpret slow growth as personal failure.

• Overwork to feel safe, worthy, or legitimate.

• Avoid selling because they associate persuasion with manipulation.

• Use “mission” to justify poor boundaries or unsustainable generosity.

• Become emotionally reactive to metrics, criticism, silence, or comparison.

• Create a company culture shaped by their unexamined habits.

The goal is not to remove emotion from business. I would never advocate for that, as one of my core beliefs is that all business is people business. As such, emotion will always be part of the equation.

Rather, the goal is to stop emotion from operating the business without your knowledge.

😊 Happiness Habit of the Week

This week’s habit:

The next time a decision feels urgent—an email that needs answering immediately, a yes that wants to leave your mouth before you’ve thought it through, a change you’re suddenly sure has to happen today—stop for one question before you act:

Is this what the business actually needs, or is this what my nervous system, my ego, or an old story is demanding right now?

One honest beat is usually enough to tell the difference.

The feeling is real either way. It matters. It just doesn’t get to make the decision alone.

Ask the question. Then choose.

This counts as self-awareness.
Even when the answer is both.

🎨 The Mosaic

The Mosaic is a collection of sources relating to the issue’s topic, that you can add to your menagerie, and widen your life, if you choose. This is the living proof in the outer experience of the premise on the inner life of the human underneath the founder.

I didn’t go looking for these five things specifically this week—they were already here, waiting for me to notice the thread running through all of them. As I was diving into the material for the issue, they came rising to the surface, one by one.

A book about naming exactly what you’re feeling instead of reaching for the nearest word. A film about a man so fixated on his purpose that he nearly misses being alive. A poem about the mirror that compels seeing something in yourself you normally don’t see while you’re busy moving through the world. A song that’s been telling the truth since 1988. And an article that gave language to something I’d been exploring my way through without a name for it.

Here’s the thread through this week’s discoveries—and why each one earned its place this week.

The Five Sources:

📚 Atlas of the Heart—Brené Brown

Brown spends the entire book insisting on precision of language (one of my ambitions, as well)—the difference and nuance between disappointment and regret, between shame and guilt, between belonging and fitting in.

That precision is what self-awareness actually runs on: you can’t tell what the business truly needs apart from what fear, ego, or conditioning is demanding if you can’t first tell the difference between what you’re actually feeling and what you’re calling it out of habit.

I am currently working my through this book, and one of my biggest realizations is how my language for my own experience has shrunk over the years—call it rote inhabiting or a very well worn path. Either way, adding nuance back into the words I use to describe my own internal experience has already helped me do the same on the outside one, too.

🎬 Soul (2020, Disney/Pixar)

Joe Gardner spends the entire film chasing his “spark,” certain that finding his purpose will finally make his life feel like it counts. The film’s real twist is that he was never missing a purpose—he was missing his own life, the one already happening while he was busy chasing the next thing to prove himself with.

Same trap, different coat.

📜Moments of Vision—Thomas Hardy

Hardy’s mirror doesn’t show you the version of yourself you’d choose. It “throws our mind back on us, and our heart,” catching a person unaware, in the tints they’d never notice while performing for the world.

That’s the harder half of self-awareness: not the self you present on purpose, but the one that shows up in the unguarded hours when there’s no audience left to build for.

🎵 Man in the Mirror—Michael Jackson

“If you wanna make a change, take a look at yourself and then make a change.” The most direct line in the whole Mosaic, and maybe the truest: before you can change anything the business is doing, you have to actually look at what’s running the show inside you first.

📰 The Psychology of Founders 2026 Edition—Founders Daily

This piece names something called “identity fusion”—when a founder’s sense of worth gets welded to the business’s performance. Its antidote, “detachment competency,” is self-awareness by another name.

What would you include in this list? Leave me a comment, and I’ll check out your source.

Leave a comment

🌿 What’s Taking Root

This week, I’ve been sitting with a line I wrote to myself in the middle of untangling all this: this is what is happening inside me. It matters, but it does not get to make the decision of how it affects me alone. The instinct to help isn’t going anywhere, and I’m not sure I’d want it to.

What’s changing is deploying the first step of actually taking a beat to listen long enough to hear what is behind the behavior, and the pause before I act on it—one question, asked in real time, instead of three weeks later.

I don’t think the two blueprints ever fully separate—the one on paper and the one built from everything I keep examining. How often I stop to ask, in the moment, is what determines which one is actually driving.

🧭 Constellation Compass

🌅 Elsewhere in the Sitting Pretty Strategies constellation recently:

🔮 Inbox Alchemy is the lab side of SPS—where ideas like these get turned into practice through a sequential, evergreen email experience—the hands-on companion to everything SPS publishes, one issue at a time. If you’re ready to move from insight to implementation, [Join us in the lab →]

🗺️ In this issue of Elegant Email Ecosystems, I made the case for framework thinking: why expertise without a container survives the moment it’s delivered and disappears the moment someone tries to pass it on, and how a signature framework turns scattered depth into something a client can actually carry forward. [Read E3 #030 →]

📡 In this issue of The Event Horizon—I made the case that tacit knowledge—the judgment built by actually doing the work, not just codified answers—is exactly what AI can’t replicate, and that protecting the entry-level work that still forms someone’s judgment matters as much as protecting what’s obviously irreplaceable. [Read TEH #004 ->]

📬 Write Back

Where has a strength of yours quietly been running the show instead of just being a strength?

Join the conversation—I’d love to hear what you noticed, and when you noticed it.

One honest line is enough. I read every reply.

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

💡The right idea finds the right person at the right time. You might be the one who gets it there.

✨ Here’s to the strengths worth examining, so they can stay strengths.

Root before you reach,
~StacyLynn
Founder, Sitting Pretty Strategies
Root Deeply. 🌳 Rise Differently.

P.S.

And if you know a founder who’s been treating a blind spot like a strategy problem instead of a personal one, send this their way.
Sometimes the mirror finds us through someone else’s hands entirely. 💌

The conversation about AI has gotten very loud. So far, in that conversation aspects about the human experience in the AI era have gotten a bit lost.

The Event Horizon is a Thursday letter built for founders who want to look clearly at what AI is actually doing—to our work, our thinking, our identity, and our businesses—and lead from that clarity rather than react from the noise.

We’re not here to chase the tools or debate the hype. We’re here to figure out what it means to stay human while building something powerful—and to do that work together, week by week.

Human lead. AI empowered. Always.

Welcome back to The Event Horizon. 🌅

This week I want to talk about a kind of knowledge that lives entirely inside people—passed from one to another, in person, over years—and goes dark the moment nobody’s left to carry it forward.

A few weeks ago, one of my favorite writers here on Substack broached a subject that not only got my internal gears turning, but also doubled the amount of time I’ve spent down rabbit holes since.

There’s a particular kind of knowledge you can’t get from books, the internet, or AI—the same knowledge entry-level work has always been built in a person.

It’s the kind of thing that lives somewhere much more visceral.

It’s in the recipe of your great-grandmother’s that you’ve tried recreating for years, and just can’t figure out what’s missing, and in your taste buds because the results you keep getting does not taste like what you remember when your grandmother made it. It’s in a parent’s ability to hear a child’s cry and know, before surveying the scene, what kind of cry it was. It’s in the way a master gardener can look at a plant and simply intuit what the right care would be. Or in the way you learn to ride a bike well—not by books and pictures, but in the way your body responds to any given circumstance while your feet are on those pedals.

As humans, we are all capable of possessing this kind of knowledge.

And, as it turns out, we’re not the only species that runs on it.

📡 The Signal

Wild whooping cranes nearly went extinct.

That’s a word most of us reserve for the likes of dinosaurs and creatures that lived millions of years ago. But in the early 1940’s, it was documented that there were less than 25 birds, in total. Through various scientific and lawful means, these graceful, striking birds were brought back from the brink of extinction, but have remained on the endangered species list.

Despite the many efforts to help these bird populations grow, by 2001 their number was still only a mere 250 birds. So conservationists started a program of hand-raising captive-bred chicks and teaching them a Wisconsin-to-Florida migration route by having them follow ultralight aircraft.

University of Maryland biologist Thomas Mueller studied eight years of the program’s migration data, and found flock accuracy tracked almost entirely with the age of the single oldest bird flying that year. A flock led by an eight-year-old veteran held the route 38% more accurately than a flock made up only of first-year birds.

The flocks were made up of the same species, with the same wings, possessing the same genetics.

The only variable between a flock that migrated safely to their winter habitat in warm, sunny Florida, and the flock finding themselves somewhere in the middle of cold, snowy Indiana, was whether an elder bird was in the formation. Guiding, mentoring, and using their years of understanding of how flying the route feels is what made all the difference in getting to their destination safe and sound.

Why am I telling you this story in a letter about human intelligence in the age of AI?

Migration looks like it should be hardwired—instinctual, coded in DNA, the kind of thing a young animal is just born knowing. It isn’t. It’s what the Hungarian-British scientist-turned-philosopher Michael Polanyi referred to as tacit (route) knowledge, built by flying it once badly and getting corrected by someone who’s flown it before. And this kind of knowledge can only pass from a bird that has it to a bird that doesn’t by literally flying beside them.

A flock of only rookies has identical wings, identical genetics, and gets lost anyway.

The moral of this story?

For humans, the knowledge pathway is the same.
And much of the research being conducted in 2026 is converging on the identical mechanism in the human workforce, and the effects AI is having on that knowledge pathway.

The Dallas Fed (Feb 24, 2026, J. Scott Davis) found AI is substituting for entry-level workers—doing codified, “book-learning” tasks—while simultaneously complementing experienced human workers whose value is tacit knowledge AI can’t replicate.

It also found entry-level hiring in AI-exposed fields down 16%, while at the same time the wages for the top decile of the experienced workers mentioned above in those same fields are up 8.5%.

A 2026 arXiv working paper on intergenerational knowledge transmission also found early evidence that automating entry-level cognitive tasks is disrupting the exact pipeline junior workers have always used to acquire this tacit expertise—by working alongside someone who already has it. Following the same mechanism as the cranes: the entry-level work was never just output. It was always the flying-beside-the-elder part that makes the difference in passing on the knowledge.

If tacit knowledge only ever travels one way—someone who has it, flying close enough for someone else to learn the route—what exactly are we building when we automate away the flying? A faster flock, or just a bigger one that’s all lost together?

🪨 First Principles

The Sum Was Never The Important Number

If you’ve been in my world for any length of time, you’ve likely come across one of my main principles regarding AI systems: use AI for the mundane and repetitive, freeing up and keeping vital human energy for the creative, strategic, evaluative—for the irreplaceable. And I stand by it.

This week, however, I want to sharpen that lens a bit.

The crane story and workplace data both show that “mundane-looking“ and “irreplaceable“ aren’t always mutually exclusive. The reality is, sometimes they just aren’t different tasks. Sometimes they’re the same task at different points in a person’s experience.

The entry-level work isn’t just output-shaped grunt work waiting to be automated. It is the actual mechanism by which judgment gets built in the first place.

So the applied version of the above position on AI isn’t only “protect the irreplaceable.” It also has to be “know the difference between a task that teaches the person nothing new (mundane) and one that’s still helping to form someone (irreplaceable).”

Recently there was an argument put in front of me on the capacity of human knowledge in the age of AI that I simply had to push back on.

One side said, “human capacity is finite, and eventually technology will outpace and control us.” The other side said, “the sum of human intelligence was never a quantity—it lives in the structure of humanity itself.”

Here’s what is important for conscious founders to understand about those two ideas: I believe there is a door number three to this argument. An aspect both sides missed, and it’s the one we, as entrepreneurs (and as humans), can play a huge role in.

Collective human intelligence was never a quantitative number to be outscaled, and it’s also not only a singular structure to be poured into, either (which, in my opinion, still puts it into a “finite”-kind of category).

The Collective Was Never a Rounding Error

The “technology will outpace and control us” crowd’s whole argument needs the sum of human intelligence to be one number, stacked in a column, so a bigger number can eventually beat it—and so the whole of it can be waved past like a rounding error on the way to something else.

The “structure of collective humanity” side’s argument is based on Harvard evolutionary anthropologist Joseph Henrich’s central idea that humans became unusually successful not because lone individuals are exceptionally intelligent, but rather because as a species we are exceptionally good at learning from one another across generations. What we refer to as tacit knowledge. But Heinrich’s concept was limited to one intelligence passed from one person to another person, through time and proximity.

I believe this side’s argument is incomplete because another scientist’s reasearch shows that there is more to this story.

Developmental psychologist and cognitive scientist at Harvard University, Howard Gardner came to understand that even inside one mind, intelligence is pluralistic—and each intelligence is distinct, shaped by what the people around you chose to notice and cultivate—even within the same human. His research started with one question, “Does the human BRAIN have anything to tell us about the human MIND?”

I think that is fitting, because the brain holds intelligence, the mind shapes it, and the collective network is where it actually lives.

Howard Gardner and Joseph Henrich, working in two completely unrelated fields, spent their careers proving that the collective intelligence of humanity was never just a cumulative number to be surpassed.

Henrich showed this at a one scale: intelligence was never housed in one skull but in the transmission network—the collective brain—specializing, teaching, and recombining across generations.

Gardner’s research expands that single skull to not just one intelligence but rather multiple intelligences, transmitting across the network, multiplying that intelligence exponentially.

The pair set up my assertion that there’s actually a third way to look at the situation. Put them together and there’s no single number left to round away, not inside a person, and not inside a species.

The Network Was Never Static

Here’s where my third door resides.

Collective human intelligence is a networked, plural structure, built person to person, like the cranes from our flock in The Signal. But something the cranes don’t have that we humans do is multiple intelligences, across a beautiful network of collective human knowledge.

It’s precisely that network that has always caught our mistakes before they became permanent while taking in the knowledge we accumulate.

Bring that all the way down from the scope of civilization to your desk now, let’s talk about how this isn’t only across generations (time), but also true of the task sitting in your inbox right now (proximity).

What you choose to do with that task matters in the network scheme of things, too.

Every time you choose to put AI to work and take that piece off your hands, you’re not only choosing speed. You’re also choosing whether that task keeps doing what entry-level work has always done—forming the judgment behind it—or stops there, finished, before it’s taught you anything.

Every time you choose technology over autonomy, you’re making a choice about whether the network stays intact or loses a node.

It’s a choice we all have to make, every day, from here on out.

Because that ‘catching mistakes before they become permanent’ part isn’t only in an existential frame, and it’s not ancient history either. It’s what’s supposed to be happening every time you review, question, or push back on something AI hands you.

The reality is, the network isn’t just “out there,” at the species scale. For conscious founders, it is running through our businesses, in real time, every time we choose to check the work, or simply accept it.

A founder isn’t just a node consuming that network’s output. They’re a node responsible for keeping it alive—for themselves, and for whoever they eventually hire, and for whom they touch with their message, products, and services, or leave a legacy for.

Hand off every piece of your own formative work to an AI system, without staying in that loop, and you’re not just moving faster. You’re also subtly opting out of the mechanism that produces the judgment your business will eventually depend on.

I’ve said before that what atrophies in a founder disappears from the business, too. This week’s version comes earlier in the chain, because what never gets the chance to form doesn’t atrophy. It simply never exists to begin with.

The Dallas Fed data above isn’t just an interesting statistic—it’s door number three showing up as a receipt.

Experienced workers, the ones already holding tacit, network-built judgment, are getting more valuable as AI absorbs codified work, not less. That’s the market pricing in exactly what Henrich’s thesis predicts: the individual was never the unit of intelligence, so the individual who’s already inside the network is worth more than ever, and the pipeline that builds the next one is worth protecting on purpose.

Which means, if you’re building alone right now, or even with a small team at the ready, the pipeline the market just priced in is standing exactly where you’re standing. For most solo conscious founders, there’s no department weighing that tradeoff.

There’s just you, deciding whether to walk it through or skip it.

The network sounds abstract at civilizational scale, all those decades and generations and hundred-million-person workforces. It isn’t, not really. At the size you actually live in, it’s the size of a shop. A relationship. A single decision you made yourself instead of handing off. The thing you took the time to understand before sending it out. That’s small enough to see, which is exactly where I want to take this next.

Because a system can retain the answer to a question while a population loses the competence to frame the question, test the answer against reality, or notice the anomalous cases.

That’s the whole risk, at any scale. Not that the answer disappears. That we just stop being able to ask.

🫀 The Human Layer

Are You Still Noticing?

Now I want to bring this down to a more local level, because that’s where we live—in the local neighborhoods we inhabit.

In July, I posted a Note here on the platform about the consequences of the loss of one local legacy small business. I made the argument that a shop closing equals a node in the network going dark, an unrepeatable configuration of judgment and discernment gone forever.

I was inspired to write that because of being part of another conversation happening in one of my favorite rooms here on Substack (or anywhere for that matter), That conversation, the Note I wrote, and my not being able to stop thinking about the judgment that just gets lost to time when any single node goes dark on the collective network all came from one of my favorite writer’s, Charlie Garcia—knowledge conservator and author of Capital Mischief.

I owe this particular thread of thought to Charlie’s piece on tacit knowledge—his reading of Henrich, Scott, and Wang is what pulled this connection into view for me in the first place, and it’s a debt worth naming instead of walking past (Thank you, Charlie, appreciate you!)

The Individual Collects The Knowledge

The thing about tacit knowledge is that it makes civilization both able to expand and at the same time it makes it vulnerable.

Expanding because real discovery, judgment, craft, interpretation, and care all exceed formal rules of process or pattern.

Vulnerable because much of what enables a society to function and flourish cannot be fully archived.

A civilization does not merely possess knowledge. It must continually re-enact it through people, relationships, environments, and practices. When those renewal loops break, a society can be information-rich and competence-poor.

And it lives at the local level of the neighborhoods we live in. ...Which family needs a little unspoken grace on the bill this month. Which supplier is honest. How to fix things nobody makes parts for anymore. Or how to make a specialty or staple no one has a recipe for because it’s handed down through generational practice.

None of that comes with a manual. Somebody had to notice it first, and keep noticing it, long enough for it to become knowledge instead of a passing observation.

That noticing has a name.

Curiosity is one of the vital capacities by which tacit knowledge is acquired, tested, reused, and eventually shared.

Curiosity is the impulse that turns an inarticulated hunch, anomaly, or gap in understanding into an inquiry. It’s what makes someone linger long enough to notice a pattern. It is also often what happens after a tacit recognition of that hunch, anomaly or gap. Curiosity transforms tacit pattern recognition from an internal knowing into action.

It acts as a kind of epistemic motor.

So while tacit knowledge gives us a feel for where there may be something to discover, curiosity sustains the willingness to enter that uncertainty rather than merely dismiss it.

Now to the lens which this publication’s edition is built on, and how it connects the pieces we’ve discussed so far.

Curiosity is a necessary trait to deploy when using AI, to remain inside the conversation—as well as the architect of that conversation—when building with this technology.

Curiosity is necessary for using AI well because it keeps a person inside the epistemic process—the verification of knowledge—rather than positioned merely at the endpoint, receiving outputs.

In this sense, curiosity is a defense against AI’s most seductive feature: its capacity to provide a closing argument or “final answer” before the human has had any genuine understanding of the output, and simply accepted it as their own comprehension of it.

To remain inside the conversation means treating AI output as a preparation, draft, hypothesis, or initial supposition or prompt to be used for further inquiry—not as the end of inquiry.

AI cannot genuinely tell a founder which client will be trouble before you’re knee-deep in it. Or when a project is slowly, almost imperceptibly, going off the rails before the metrics show the in-your-face evidence. It can’t tell a founder what to prioritize when they are under pressure and need to preserve their vital energies. It cannot help you know who the right hire is because the answer is always beyond the resume and the CV.

And AI can’t truly recognize a performative argument—another person’s or its own—when what the founder really needs is push back and real discussion and debate.

These are skills and knowings a founder builds over time of doing the things repeatedly, over time—sometimes badly, learning the lesson, and bringing it forward for the next go round.

None of that is a case against using AI.

On the contrary, it’s actually the opposite. I believe a founder who stays curious alongside it doesn’t get a smaller version of the tool—they get the version that genuinely works: AI doing the fast, tireless first pass, and a human still awake enough to catch what it missed, question what it got confidently wrong, and push one round past the first answer.

That’s not slower work. That’s the partnership actually working.

🌅 Light on the Horizon

The Renewal Loop, Working As Intended

The Nodes That Stay Lit

Here’s the detail that makes this one work: the communities themselves are the ones deciding what’s worth keeping and how AI gets used to keep it.

Across Indigenous communities in Aotearoa/New Zealand and beyond, AI is being used to organize, retrieve, and help regenerate oral and ecological knowledge that has always lived in people, not archives. Te Hiku Media’s Kōrero Māori project is one of the clearest examples—language and knowledge recordings processed under a Kaitiakitanga license, a framework that forbids misuse of the data and lets the people who gave it withdraw it whenever they choose.

AI does the retrieval work at a scale no single elder or archivist could manage alone. The community still decides what’s worth keeping, and how it is kept.

It’s this whole issue’s argument, running in reverse.

Instead of a node going dark when a shop closes or an elder passes, here’s a network actively choosing, on its own terms, which nodes get to stay lit—and building the infrastructure to keep them that way. Read more →

Before the Knowledge Walks Out the Door

A newer cousin of the same idea, happening in manufacturing plants right now.

Systems built on a 2025 research prototype called OAK are being used to capture what a retiring machine operator knows before their knowledge, and more importantly their hard won judgment, walks out the door with them—narrated failure videos, shadow-shift notes, the sound a bearing makes two weeks before it fails.

None of that lives in a manual.

A new hire can now ask, in plain language, what to check first when a specific fault shows up, and get an answer built from the retiring operator’s actual judgment instead of a generic troubleshooting guide.

Here, AI’s job is the mundane, repetitive work: organizing and retrieving what the operator already knows, so the judgment itself has somewhere to go besides retirement.

It’s First Principles’ argument, put into practice.

Automate the retrieval. Protect the forming. …The tacit knowledge still had to be built by someone doing the work for decades—AI just makes sure it doesn’t disappear the day they stop. Read more →

⚠️ Concern Worth Naming

Something worth watching:

AI is already infamous for its overly atta-boy attitude. Relying on it too heavily can weaken tacit knowledge, reducing the chances to build lived pattern recognition in the first place. It can also weaken curiosity, making it too easy to accept a fluent first answer as fact and closure rather than a jumping-off point worth staying with a little longer.

Run that same shortcut across an entire team leaning on the same handful of models, and the risk doesn’t stay personal.

A 2026 SSRN paper on AI-driven epistemic homogenization found that when everyone consults the same systems, collective judgment erodes—invisibly, without the social pressure classical groupthink usually requires. Not one person’s pattern-recognition thinning. A whole network’s plurality, converging toward the same blind spot.

The good version of this looks like:

Founders, and their teams, who treat curiosity as a practice and not a personality trait—staying with AI output long enough to actually question it. That single habit does double duty: it keeps individual pattern-recognition alive, and it keeps the collective pool of judgment genuinely plural instead of narrowing toward sameness.

That curiosity is the loop. Stay in it.

🎯 Your Move

Name one piece of knowledge you carry that you’ve never fully written down—the call you make on instinct, before you could ever explain it in words.

This week, before you hand that particular judgment to AI to save time, make the call yourself instead. Go slow enough to actually notice what you’re drawing on when you do.

That noticing is the whole practice.

It’s you, flying the route once more, on purpose, so you stay someone who still can.

🧭 Constellation Compass

🌌 Elsewhere in the SPS ecosystem recently:

🗺️ In this issue of Elegant Email Ecosystems, This week I made the case for framework thinking: why expertise without a container survives the moment it’s delivered and disappears the moment someone tries to pass it on, and how a signature framework turns scattered depth into something a client can actually carry forward. [Read E3 #030 →]

🌱 In this issue of The Master Work, I sat with the fact that becoming who I want to be was never going to be a straight line—the brain science behind how a choice becomes a habit, and why the return matters more than the unbroken streak. [Read TMW #002 →]

🔮 Inbox Alchemy is the lab side of SPS—where ideas like these get turned into practice through a sequential, evergreen email experience—the hands-on companion to everything SPS publishes, one issue at a time. If you’re ready to move from insight to implementation, [Join us in the lab →]

📡 Signal Back

This week’s question:

What’s the route only you know how to fly? And who, if anyone, is close enough to learn it from you before it’s gone?

Reply and help me see the whole picture.
One honest line is enough. I read every reply.

Leave a comment

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply.

Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread (this issue is proof of that).

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

🌿 The Understory

What you just read is the argument. What follows is the work.

What comes next was built for The Understory—and it goes where this issue has been pointing all along: into the practice of actually forming the judgment I just spent this issue arguing you can’t get any other way.

This week’s Understory is three things. An Extended Reflection Set—five questions built to take this past the intellectual case and into the parts of your own life and business where a node might already be going dark. A Practice Protocol: “Narrate the Call“—a simple, week-long habit of narrating one judgment call before you make it, so the noticing underneath your instinct finally gets a record. And an Integration Frame—four places in your actual business, content, hiring, client relationships, systems, to build the finished-mundane-versus-still-forming distinction in, not just think about it.

And at the close—a brief, honest note on how I used AI this week. This is a standing section in every The Event Horizon Understory, because my position on AI is simple and steady, and now refined: use the tools, stay in the loop, notice the pattern—stay in the lead. These are not in conflict. The Understory is built on exactly that premise.

Let’s get to work.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →]

✨ Here’s to flying close enough to learn the route—and to becoming the elder worth following when it’s your turn to lead.

Stay curious. Stay human.

~StacyLynn
Founder, Sitting Pretty Strategies
Root Deeply. 🌳 Rise Differently.

P.S.

And if you know a founder who’s been quietly carrying a whole route in their head—the thing only they know how to fly—forward this their way. It might be time to fly close enough for someone else to learn it. 💌

Welcome back to Elegant Email Ecosystems, where we believe legible thinking is what actually earns a room’s trust—and a framework is simply thinking made legible.

Last issue, we talked about the instrument panel that reads what’s already true across your whole business’s ecosystem. This week, we’re zooming even further out—to the frame that decides what’s worth building an instrument for in the first place.

I once had a mentor that thought frameworks were a founder’s superpower. He saw them as a compact, portable device used to make big heavy pictures small enough for a prospect to fit in their back pocket.

That’s what a framework does.

A framework allows your client to carry a small representation of your big idea out into the world for you without burdening them with its inherent complexity.

I’ve taken that belief and run with it. From early on, in building Sitting Pretty Strategies, I built out frameworks to help explain the big ideas I have in marketing and business. And I noticed something as I did.

Thinking through frameworks helps you carry the complexity of your big ideas and still be able to explain it to someone without bogging them down in the details right off the bat.

If you’ve read Elegant Email Ecosystems (E3) much at all, you’ll notice that I even have a dedicated, albeit rotating, section in the newsletter for the framework I am working from that week. That’s how much I believe in the power of them.

So the question you might be asking is, “Okay, but aren’t frameworks for the big guys/gals with books, and huge followings, and million dollar businesses?”

I contend that the framework is exactly how they got where they are–and you can, too.

Ready to get your superpowers?

Expertise Without a Container

As I discussed in the Find Your Frequency First issue of E3, Visibility is one half of the equation of being found by the audience you are looking for.. And for many founders, it is the more challenging of the two components.

They have deep expertise, and they may have even cultivated their voice into a consistent, reliable asset. These are not the challenges they face. What trips this kind of founder up is wrangling all of their expertise into packages their ideal clients can actually receive, rather than scattered across the known interwebs like pages of a book being dropped from an airplane.

They might find a few pieces, but fragments a whole picture does not make.

The gap for them isn’t knowledge—it’s structure. Founders with real depth and no structure around it still explain themselves differently every time, because nothing’s holding the expertise in a repeatable shape.

What does this have to do with visibility?

Well, the brand who gets repeat business at scale—real visibility—is known for something specific, something consistent, and that something is unique to them. It’s the thing that separates them from all of their competition in the same industry or niche.

It’s a repeatable asset that your clients can understand and they themselves can describe in definitive terms.

And more than that, it gives clients a picture they can put in their back pocket and easily pull out when they need to remember you—to help them solve a problem they have, refer you to someone they know, or promote your work to others who could benefit from your expertise. Your packaged picture helps them know how to talk much easier about what you do, how you help, and whether it’s right for a specific moment.

🎙️Real Founder Confessions

As I mentioned above, a mentor from early on in my marketing journey laid the groundwork for what would become an asset (and a skill) I lean heavily on in building my business and my expertise. And I am forever grateful.

Before this distinction in how expertise and knowledge is passed on to prospects and clients was made clear to me, I inherently noticed it in the marketplace. I knew some founders had unique, memorable ways of describing what they do, while others spoke at length about what they did and how they helped, but nothing really stood out to differentiate from anyone else who does what they do.

You’ve likely encountered both yourself, maybe without recognizing there was a real difference you couldn’t quite name.

Here’s what it often looks like: a founder with real depth in their work walks into every opportunity to describe what they do and how they help—every pitch, every discovery call, every piece of content—and is starting from zero.

They’re explaining their approach a little differently each time, depending on who’s in the room, or which idea feels sharpest that day. The expertise is real. But nothing about how it’s presented is chunked down for the average consumer in their niche—the big idea is still big, and hard to digest in one sitting or brief encounter.

And nothing about how it’s presented is repeatable.

So nobody who meets them once can turn around, and in concrete terms anyone can understand, repeat it to somebody else. They completely skipped building a frame around what they know and went straight to talking about it.

The result?

People like them, remember liking them, and still can’t say what they actually do.

What’s the moral of this story?

Expertise without a frame survives the moment it’s delivered, and disappears the moment someone tries to pass it on.

A Frame Turns What You Know Into Something Someone Else Can Hold

Okay, time for a little brain science (if you’ve been reading for a while, you know I like a little brain science with my marketing. 😁).

The human brain takes in approximately one billion bits per second but only processes ~10 bits per second. And of that 10 bits per second we are actually catching, we can only hold about 7 pieces of information at any one time. George Miller, the Harvard scientist who discovered this through cognitive load experiments, and posited that people could store more bits of information if they were able to chunk (or combine) some pieces of information together. Cool, eh?

So right about now you may be wondering what the heck the brain’s ability to hold more information than otherwise if some of it is chunked together has to do with a frame that turns what you know into something someone else can hold.

Well, I contend that a frame is a form of chunking—allowing the combination of pieces of your expertise to be combined in ways that a client’s brain can hold onto it longer. And a framework is what simplifies the complexity of it into a form they can repeat to someone else, or understand for themselves.

A framework is how a founder, expert, or brand turns their internal strategy for getting their clients, students, or customers results into a repeatable, memorable external story. When used in this way, everywhere that message lands ensures reinforcement of the same idea.

So, it is the back-pocket picture your client uses as a guide to understanding your expertise, and the structured problem-solving approach you use to help them.

Compressing and organizing your expertise into a framework—or a series of them—makes that knowledge and skill portable and uniform, across all of your channels.

How You Know If There’s No Frame

A missing frame doesn’t just announce itself. It doesn’t show up as an obvious knowledge gap or a bad meeting.

Rather, it shows up as a string of good moments that never seem to add up to anything the founder can point to and say, “that’s the thing I’m known for.“

Here are some examples of a missing frame in a few real-world situations:

The Pitch That Changes Every Time
Ask an expert what they do twice, a week apart, and you get what feels like two different answers. It feels that way because there’s no fixed shape the explanation is supposed to take.

As mentioned above, whoever’s asking gets whatever version feels sharpest that day.

Liked, Not Remembered
The founder gives a keynote, and knows it landed. The expert has a call with a potential client, and it goes well. The brand puts out a winning post, and it gets real comments.

And a week later, the person who sat through the keynote, participated in the call, or loved the post couldn’t summarize what was actually said if you asked them to. Real resonance happened. But genuine retention didn’t.

The Sales Call Starting From Zero
Every time the founder takes part in a discovery call, they have to rebuild the case for their solution from the ground up—the credibility, the value, the reason to trust them over anyone else—because nothing the prospect encountered before that call did any of that work in advance.

Content With No Throughline
Individually, a brand’s posts are good. Some are even very good. Together, however, they don’t build anything.

A follower could read six months of content and walk away with six unrelated impressions instead of one clear, compounding position.

Each of these situations looks different on the surface. Underneath, though, it’s the same gap: real expertise, no frame holding it together—so nothing about it can travel, compound, or be repeated by anyone but the person who said it first.

Framework Thinking Is a Discipline, Not a Personality Trait

Mental models are shortcuts we humans use to understand things in the world. Framework Thinking is one such model.

Framework thinking is a kind of mental model that organizes your thoughts around a given topic or idea into patterns that are easier to hold on to than scattered ones circling around the same things but not quite connecting. In essence, it packages said ideas and thoughts into a framed picture you can hold in your back pocket until you need it.

Thinking in frameworks is genuinely useful because frameworks simplify complexity, build credibility, and anchor leadership positioning.

It helps you explain your expertise in language that easily lands for your audience, it helps your audience make sense of your expertise, and be able to share it with others.

Real situations—with all the moving parts and nuances—hold too much information to hold in the mind at once. Especially when you add the aggregate of an average person’s intricate life. A framework performs a kind of selective compression on the organization and structure of difficult to analyze or understand information.

A Framework Simplifies Complexity
As I went over in our brief brain science lesson, the brain can only hold so much at once—somewhere around five to nine pieces of information, before it starts dropping things.

Giving your knowledge a framework to live inside is what takes a sprawling body of expertise and compresses it into something that actually fits into a container that is tangible and manageable. It acts as a truer shape for the same truth to travel in. Once the frame exists, a founder can continuously and consistently pull from what was already built, and expand it however the room requires, instead of reconstructing the whole argument from the ground up every time.

A Framework Builds Credibility
Saying the same true thing the same way enough times stops it from sounding merely like an opinion. It begins to sound like genuine expertise. That’s what happens when someone has clearly done the sorting and refinement already: decided what matters, what doesn’t, and how the pieces fit together.

A founder who explains their work differently every single time sounds like they’re figuring it out in real time. A founder with a framework sounds like someone who already has.

A Framework Anchors Leadership Positioning
Whomever sets the terms of a conversation is the one who leads it. A named framework does more than simply describe the work. It hands the room the language to talk about it, and once people are using your terms, you’ve stopped being one option in a comparison and started being the reference point everyone else gets measured against.

That kind of leadership gets claimed by being the frame other people end up borrowing.

A framework centers around one complex idea, broken down into simpler parts, clarifying the relationship between those parts, and presenting them in a way that offers how to use the parts consistently. In doing so, it tells people what to focus on in a particular situation, what to ignore in that situation, what order to think it through, and what a complete response to the situation requires.

On a business level, a framework differentiates you from others who do what you do, and when done well, it becomes what you are known for, and what sets the bar in your niche.

🏠 The SPS Philosophy: Your Framework Is Your Signature

Like a person’s fingerprint, the skyline of a city, or a scent trail only a dog can smell, a framework can become your brand’s distinctive signature.

A fingerprint lets you be recognized without having to prove who you are repeatedly. A skyline tells a stranger exactly where they’ve landed, at a glance. A scent trail exists purely to be followable, for whoever’s trying to find their way.

A framework does the same kind of work.

It gives the person on the other end something they can recognize, hold, and pass along, even when you’re not in the room to explain it yourself. That’s the real test of a good framework: how much of it the other person can still carry once you’ve left the room.

That’s the part of framework-building that gets missed most often: the frame’s real job is lowering how much work everyone else has to do to hold onto what you know, while also lowering how much work you have to do to make yourself understood.

Every piece of expertise handed over without a frame around it is a piece someone else has to organize themselves, carry themselves, and try to recall accurately later, on their own.

A good signature framework does all of that work for them.

At Sitting Pretty Strategies, that’s the belief underneath every framework we build or teach: a real framework is an act of generosity toward the mind that has to carry it. The clearer the frame, the less the listener has to work to keep what you gave them—and the more of it they actually keep.

Because real expertise deserves a shape simple enough for someone else’s mind to hold onto, remember, and repeat, well after the conversation with you has ended.

A signature framework checks all of those boxes.

So, build the frame for the person who has to carry it forward, and the credibility, the memorability, the leadership positioning we just walked through will become the natural result.

🧐 Upon Reflection: A Signature Framework is Your Brand’s Fingerprint In Motion

The real superpower is building something worth repeating.

Every idea in this issue comes back to the same shape: a big, sprawling body of expertise, folded down small enough to fit in someone else’s back pocket, and durable enough to survive the trip once you’re no longer there to carry it for them.

Framework thinking is a discipline—the same one you’d use to sort a cluttered room into shelves someone else could actually find their way around. The frame does the sorting once, so your audience receives it already organized.

Every metaphor in this issue—the mentor’s superpower device, the fingerprint, the skyline, the scent trail—says the same thing in a different voice: build a useful tool for the person who has to carry your expertise when you’re not there, and the credibility, the memorability, the leadership positioning show up as what the framework naturally produces.

If you walk away from this issue holding onto only one thing, let it be this: the frame is the shape that finally lets your expertise stay in motion and travel without you.

🤔 Your Turn To Reflect

Sit with this for a minute—an honest gut check on what you’re actually bringing out into the world right now, and how well it travels once you’re not there to carry it yourself.

1. If someone asked you to explain what you do right now, would you say the same thing you said last week?

2. Where in your business are you still explaining yourself from scratch, every time?

3. What would you have to leave out of your message to make the rest of it unforgettable?

One honest answer to any of these is enough to start—whether that means building your first frame or sharpening one you’ve already built. Either way, you already have the expertise. What’s left is giving it a shape someone else can carry.

🌀 The Ecosystem Pulse

Before you go—one honest question: Did this issue nourish something?

Reply with a single letter and let me know how this one feels for you:

A — Yes, I feel more aligned

B — I’m still sitting with it

C — This one didn’t connect

Every signal strengthens the ecosystem. Thank you for being part of it.

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

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④ Drop a comment—I read every one. And I reply. Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread.

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💡The right idea finds the right person at the right time. You might be the one who gets it there.

🌿 The Understory

What you just read is the argument. What follows is the architecture.

For Stewards of The🌳Grove who have subscribed to The Understory: what’s waiting for you below is built directly from this week’s concept. The F.R.A.M.E. Formula for building out your own signature framework, walked step by step. A full worked example, built start to finish, so you can see exactly how one scattered body of expertise becomes one repeatable message. And a set of AI-assisted prompts built to help you run the process on your own expertise this week.

And at the close, I’ll go into a brief, honest note on how I used AI to write this issue. This is a standing section in every Understory issue, because SPS’s position on AI is simple and steady: offload the mundane and repetitive so your irreplaceable creative energy stays where it belongs, in the work only you can do. These tools are built for exactly that.

Let’s get to work, Stewards.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →

✨ Here’s to the frame that carries what you know, long after you’ve left the room.

To the work that lasts,

~StacyLynn
Founder, Sitting Pretty Strategies
Root deeply. 🌳Rise differently

P.S. You know a founder right now who’s brilliant at what they do and still can’t say it the same way twice. Forward this to them—sometimes the frame someone needs shows up as something a friend hands them at exactly the right moment.

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Welcome back to Elegant Email Ecosystems, where we believe an ecosystem sends four signals about your business at once, all the time, and the founders who see their businesses operate with more ease and elegance are the ones who learn to read them together instead of arbitrarily or as unconnected pathways.

In the last issue of E3, we ended on a question about orchestration—whether your voice and your visibility were actually working together, or just happened to be occupying the same business at the same time.

This week, we’re zooming out from that one relationship to the entire instrument panel it’s sitting on.

In modern business, there’s always been a pull toward the easiest signal to measure.

Revenue’s up, so the ecosystem must be working. Opens are steady, so the relationship must be intact. Under that kind of framing, though, founders tend to land in one of three places:

⓵ They check the single indicator that’s easiest to see—usually conversion, because it’s the one with a dollar sign attached—and base an assessment on whether that number looks like what they hoped it would.

⓶ They run a real, thorough audit of one area at a time—fixing the welcome sequence this quarter, the launch sequence next quarter—while the other signals go completely unread for long stretches of time, if at all.

⓷ Both ⓵ and ⓶, plus the certain type of exhaustion brought about only in finding out something’s wrong after it breaks—a launch that vastly underperforms, a list going from tepid to downright icy—and having to trace the damage backward instead of catching it on the panel before any havoc is wrought.

Often under these circumstances, founders reach for the metric that’s most legible: whatever’s converting.

If the sales are coming in, they reason, the rest of the ecosystem must be holding steady. They chase the number they can readily see and assume the numbers they can’t see as easily are following along behind it.

The conversion piece is real, and it deserves genuine attention.

An ecosystem, however, sends four signals at once, all the time—which means checking one gauge on the panel and assuming the rest are fine is a natural but false comfort.

So the full picture—how connection, consistency, conversion, and continuity are all actually behaving, in relationship to each other, in real time—isn’t something one dashboard, however good, has ever been built to show you.

So, where do you land: are you flying on a single gauge, no instruments at all, or four gauges that have simply never been read together?

One Good Number Does Not a Healthy Ecosystem Make

One number in an acceptable range doesn’t mean you have a thriving ecosystem. It just means the signal you’re watching happens to be fine at the moment.

The real problem arises when founders are only checking one part in the ecosystem and calling the whole thing good. It’s a bit like a pilot watching only one gauge during a flight and assuming they will get to their destination intact and on time from that single piece of information.

When you read only one of the signals your business is sending, you are doing the same thing, and mistaking it for reading the instrument that maps the whole picture. Examples are reading open rates and assuming health, or reading sales and ignoring the silence across channels.

If your revenue’s momentarily robust, your open rates are strong right now, or onboarding feels smooth enough, it’s natural to read that one signal as proof the whole ecosystem is healthy and humming along. The inclination to rely on whatever’s easiest to view is a human instinct and happens to careful founders as easily as anyone else.

An ecosystem rarely fails all at once, though.

It fails one small signal at a time, while the one you happen to be watching keeps reading just fine. By the time the indicator from the struggling component finally makes itself impossible to ignore—attribution fog, repetition at scale, a client who vanishes after the sale—it’s already been failing for a while.

One visible signal can start to stand in for the health of the whole ecosystem, while things are actually lagging in the background.

A Single Signal Tells You Nothing

A single reading only tells you about the one thing it’s set up to watch for.

Real diagnosis happens when all four signals converge, creating a solid snapshot of the moment at hand—from the same moment, reviewed against each other. That’s the shift underneath everything ahead: checking one signal against the performance of the entire ecosystem and looking for conditions of similar results.

When something feels out of alignment, most founders run an audit.

The welcome sequence isn’t converting, so they rewrite the welcome sequence. The list’s gone cold, so they build a re-engagement campaign. A launch flops, so they rework the launch. Whatever’s visibly broken gets isolated, fixed, and moved past.

There’s real logic in this.

A well-rewritten welcome sequence probably does perform better. A thoughtful re-engagement campaign likely will wake a few people up. Treating the symptom where it shows up isn’t wrong, in theory.

It’s just incomplete.

And it produces a particular kind of fatigue: the founder who’s fixed everything at least once, and still finds themselves back in the same territory eighteen months later. Only now they’re troubleshooting a different piece with the exact same isolated method and experiencing a distinct form of bandwidth-consuming frustration.

Throwing fix after precious, energy-eating fix at one signal was never going to tell you anything about whether the other three were still rooted and sustaining while your attention was somewhere else.

Simply because an audit is retrospective.

It happens after something’s already gone wrong enough to notice. By the time you’re auditing, the problem has had months to compound in whatever corner you weren’t looking before.

A reading across a meta-layer diagnostic is different. It’s live. It runs continuously, on a single touchpoint, in real time, whether or not there’s a symptom yet to justify checking.

That’s the name of the game this week: reading what’s actually happening before it has the chance to become obvious at all—instead of getting faster at fixing what’s already broken.

Because when you don’t have the right equipment to measure success by, you are flying blind in your business and not even knowing it.

That’s exactly the trap I fell into myself.

🎙️Real Founder Confessions

I used to operate solely by instinct, reacting to whatever signal appeared to be most urgent–or interesting, with no instruments telling me what actually needed attention or when.

In an early issue of E3, I told a story about when I first started building Sitting Pretty Strategies. I was holding everything I was building between my ears and in a notebook that quickly became muddled and incomprehensible.

It was not a pretty situation at all.

But it was part of my journey to learning about the power of systems. It was also a powerful lesson in the value of structure. See, I was always the queen of flying by the seat of my pants—and I came out on top enough to think it was a strength. And sometimes in life it can be.

But in business, it became my biggest liability.

For years I resisted structure, put out whatever fire was most severe, and after cycles upon cycles of that, eventually realized structure wasn’t a cage—it was actually sanity-saving liberation.

As it happens, now I can tell you that story knowing that it’s literally the pre-instrument state: no gauges, no panel, just reacting to whichever signal screamed hardest in the moment, usually ending up the wrong one to address.

Let me back up for a second and just talk through this with you, the way I would if we were all in a room together and someone asked, “Wait, what do you actually mean when you say ‘signals from an ecosystem’‘?“

Here’s how I think about it. Tell me if this tracks for you.

Every relationship your business has with a subscriber, a follower, a client, moves through four basic things it needs. Doesn’t matter the platform, the price point, or the source. Whether it’s a nine-dollar download or a thirty-thousand-dollar retainer. Same four things each time. I call them the 4 C’s, and once you see them, you’ll start noticing which one your business tends to skip.

① Connection

This one is simple, but not easy: does this person feel like you actually see them, or do they feel like a name pulled off a list?

Think about the last time a brand welcomed you somewhere. Did it feel like “oh good, you’re here, let me show you around,” or did it feel like a form letter with your first name dropped into a merge field? That’s Connection. It’s presence. Making someone feel like their yes to you actually got noticed.

Where have you felt that, on either side of it?

② Consistency

This one’s about whether you’re recognizably you, every time someone hears from you, more than it’s about how often you show up. You can email once a month and be wildly consistent, if it always sounds like the same person with the same point of view.

Or you can email every single day and be completely inconsistent, because the voice is in a different costume every time. Consistency is reliability of presence, not volume of noise.

③ Conversion

This is the one everyone thinks about when they think “marketing,” and that’s fair enough. It matters, of course. But conversion is really just what happens once Connection and Consistency have already done their job, and you ask, clearly, at a moment that actually makes sense.

Conversion done well feels like leading someone to the door they were already looking for.

④ Continuity

This is the one almost nobody talks about, which is wild, because it’s the one that actually determines whether you keep the relationship you worked so hard to build. And it has the biggest bottom line potential, to boot.

What happens after the yes? After the sale, after the launch, after the big exciting moment? Continuity is whether you’re still there once the transaction’s over, the thread staying intact instead of snapping the moment the sale closes.

So: Connection, Consistency, Conversion, Continuity. Four things every relationship your business has needs, whether you’ve built them on purpose, or not.

Before we move on, here’s my question for you: if you had to guess right now, which one of the four is your business leaning on hardest to carry the other three?

Most founders do, without even realizing it. (When I first started, it was Consistency. Then it was Connection.)

That’s usually where the trouble starts.

Okay—that’s how I’d explain the plain version. Here’s where it gets interesting… I built an instrument out of it, and here’s why the question above isn’t enough to get you the real information you need..

One Reading, Four Numbers

A cockpit instrument panel works like this: with one look, you can get every gauge reporting back from each of the important systems running during flight, all at the same exact moment—fuel, altitude, airspeed—all read together, because all four were always true at once, whether anyone was watching or not.

An ecosystem works the same way, even if there was no instrument for it.

There are four signals an ecosystem gives off constantly, and reading any one without the others produces false confidence—or false alarm—in the soundness of it.

Those constant signals come from the 4C’s we talked about above: Connection, Consistency, Conversion, and Continuity.

Right now, in your business, someone is receiving something from you—an email, a sequence, a single touchpoint—and in that exact moment, it’s either making them feel recognized, or it isn’t. It’s either holding the same voice they’ve come to expect, or it’s veering astray. It either offers them a door they can walk through, or it simply doesn’t. It’s either continuing a story they’re already inside of, or it’s starting over as if they’re a stranger, when they are not.

All four of those things are true or untrue at the same time, in the same touchpoint. They’re readings—live, continuous, updating with every message that goes out—whether or not anyone’s watching them.

When a pilot flies “by the gauges,” they use only the instruments in the panel in front of them as the primary source of truth whenever they are flying in conditions where their visual references are unreliable. Instrument flying is less about staring at one gauge and more about scanning and cross-checking the input from all of them across the panel.

The pilot continuously interprets the data from the gauges to course correct and arrive at the intended destination.

The founders who build ecosystems that sustain and thrive learn to stop treating the above customer journey situations as four separate questions asked at four separate times. Like that pilot, they start treating them as one interwoven output, read all at once, telling the truth about where things actually stand—today, not last quarter.

So, what if the problem was never effort, but rather it was flying without a proper instrument panel in the first place?

What It Looks Like When a Gauge Goes Unread

First, we need to address the pear-shaped elephant in the room. If the fires I see aren’t the problem, what should I be looking for?

An ecosystem operating on a partial instrument panel, or no panel at all, rarely looks broken from where the founder is standing. That’s what makes it dangerous, and why the condition can progress for so long undetected. The stimulus they’re watching for keeps appearing to read as fine, because what they see is in acceptable limits.

It’s a bit like yelling at the smoke alarm for being dramatic while the toaster is actively on fire in another room.

The trouble is always hiding in the other three indication areas nobody thought to also check.

Here are some ways the misalignment in this meta-layer can present:

The Warm Welcome, Cold Middle

A welcome sequence can do everything right—warm, specific, genuinely well-crafted—and still leave a six-month subscriber feeling like a stranger. The founder built real recognition once, at the doorway, and then simply stopped there.

Connection is a thing you keep doing, over and over, for as long as the relationship exists—and the gauge only reads as healthy for as long as you keep doing it.

The Reliable Ghost

Some founders never miss a send. The newsletter goes out every week, right on schedule, dependable as a heartbeat—and somehow still says almost nothing about who’s actually writing it.

The calendar is consistent. The voice inside it, however, has drifted toward whatever’s easiest to produce at volume, and reliability gets mistaken for health because the schedule never slipped.

The Interested, Not Invited

Attention isn’t the same as an open door. A founder can have healthy open rates, real engagement, genuine interest built up over months, and still never make it easy to say yes to any kind of offer.

The reader gets all the way to wanting something, and then has to go looking for how to get it. That’s an indicator that nobody built a door..

The Silent Handoff

A launch can go beautifully and still be followed by crickets. No onboarding, no acknowledgment that anything’s changed, no bridge from what just happened to whatever comes next.

In this instance, the relationship keeps existing on paper, even as it stops being tended. But in practice, the founder doesn’t even notice until the next launch frustratingly flops for reasons that trace straight back to a handoff nobody built.

So, by now you might be asking: what would it even take to read all four at once?

What A True Reading Looks Like

We’ll get there in the next segment, but first let’s talk about where most founders start to get the mechanics wrong without realizing it, so that the actual mechanic, when I show it to you, lands as a correction instead of just a nicer-sounding idea.

A strong Connection indication from January and a strong Consistency score from June don’t add up to a healthy ecosystem today. They’re readings from two different moments, on two different touchpoints, and neither one tells you anything about right now.

A trustworthy signal only exists when all four numbers come from the same touchpoint, at the same time—one email, read against all four gauges at once.

Four numbers.
One moment.
One source.

This is the true reading of your business in real time.

This is a harder standard than most founders are used to holding themselves to, as well as the only version of the read that’s actually true. A touchpoint is either serving all four right now, or it’s costing you somewhere you’re not tracking—and averaging across time, across separate audits, across only part of the read can’t tell you which.

Why Pilots Never Stop Checking—And You Shouldn’t Either

Here’s the part that can trip founders up: the reading never actually ends, because the ecosystem never stops producing new touchpoints to read.

A founder can run one excellent full Four-Gauge Read this month, get four strong numbers back, and feel genuinely reassured—and then send forty more emails before they think to check again. Every one of those forty is its own touchpoint, its own moment, its own four numbers, and every one of them needs its own reading, regardless of how good last month’s numbers were.

That’s just what a live, in real time reading actually means. Fuel gauges don’t stay accurate from the moment you last looked at them, either—they’re only telling you the truth about right now, which is exactly why pilots keep looking.

The panel doesn’t get built once and trusted forever. It gets read, again and again, for as long as the ecosystem is producing anything at all.

And hopefully, if you’re like me, building a legacy business, that’s for a long time.

🏠 The SPS Philosophy: Hidden In Plain Sight

At Sitting Pretty Strategies, the foundational work we continue to recommend as high-priority has always included a piece most founders skip past: actually looking at what’s true, on purpose, before something forces the looking.

Every touchpoint in your ecosystem is already telling you the truth, in real time, whether or not anyone’s built something capable of hearing it. The four signals exist whether you’re checking them or not, which means the real choice was never about whether the truth is there. It’s about whether you’ve built yourself an instrument that can actually hear it.

Reactive marketing checks the gauge after something breaks. It waits for the launch to underperform, the list to go cold, the client to vanish, and only then goes looking for what happened.

Stewardship, on the other hand, reads the panel before any of that, treating the relationship as something worth checking on for its own sake, because of its overall and inherent value to the business. They do this whether or not the numbers are currently demanding attention or explanation.

That’s the actual difference between managing an ecosystem and stewarding one. Managing responds to what already broke. Stewarding notices before it does.

A weak reading is information you asked for on purpose—which is already more than most ecosystems ever get.

🗺️ Strategic Insight: The 4 C’s SS Console

That’s the belief. Here’s the instrument built to put it into practice—the one this whole issue has been pointing toward, named in full for the first time: The 4 C’s SS Console.

The 4 C’s SS (Simultaneous Signals) Console

Meet the panel: four gauges, each one continuous, each one true or not true in every single touchpoint your ecosystem produces, regardless of where any one subscriber happens to be in their journey.

Connection reads whether the person on the other end feels recognized right now, not just on day one. A six-month subscriber deserves to feel as seen as a new one, just differently.

Consistency reads whether your presence stays recognizably you, from the first touchpoint through the fiftieth—whether the voice that shows up today still matches the one they came to trust.

Conversion reads whether a clear, legible next step exists wherever someone happens to become ready to take it, whether or not that touchpoint was designed to sell.

Continuity reads whether the thread holds at every transition in the relationship, not just the one specifically after a purchase—welcome to nurture, nurture to launch, active to quiet to re-engaged.

Read together, from the same touchpoint, at the same moment, these four gauges do something none of them can do alone—they turn a status update into a next step. A weak reading on any one gauge points somewhere specific: toward the deeper work built to strengthen exactly that signal. That routing is where this gets practical, and it’s exactly what’s waiting for you in The Understory.

🧐 Upon Reflection: Simultaneous Signals Readings Equals System Strength

A strong number in one place only ever tells you about that one place. The rest of the panel keeps sending signals, even if you’re not checking for them.

Also, a single touchpoint can pass one gauge and fail another, and most businesses never find out, because they only ever measure the gauge they went looking for.

This is why the audit model breaks down.

The shift starts with a different question. Instead of asking how an email performed, asking what it would score on all four gauges at once is much more powerful and insightful.

One instrument, read consistently, replaces those four separate audits run on four separate schedules—telling you the truth about where things stand right now, and exactly which deeper work is actually calling for your attention.

A weak reading is a direction. It’s the panel doing exactly what it was built to do: showing you where to look, before you have to find out the hard way.

Treating your ecosystem like one continuous relationship, instead of four separate machines that happen to share a mailing list, is the whole shift. It reads true, or it reads false, on every single send.

🤔 Your Turn To Reflect

Take a moment here before you move on. Everything this issue has named—the four gauges, the shift from auditing to reading, a panel that never finishes checking itself—it all comes back to the same place: whatever’s actually happening in your ecosystem right now, in this exact moment, whether or not you’ve been watching for it. These questions are where you bring that home.

Consider them an invitation to look honestly, not a scorecard to fill out.

1. Which gauge would you be most afraid to actually check this week?

2. When’s the last time a strong reading in one area convinced you everything was fine—when it wasn’t?

3. If you could only trust one gauge right now, which one would you want it to be, and why?

One honest answer to any of these is enough to start.

The panel is an instrument you keep reading, again and again, for as long as the ecosystem is producing anything at all—not something you check once and trust forever.

🧭 Constellation Compass

🌌Elsewhere in the Sitting Pretty Strategies constellation recently:

🌅 In this issue of The Event Horizon, I made the case that the real debate under Substack’s new AI-detection feature is about who still gets to decide what’s good, valuable, and worth our attention, and why that verdict was never the machine’s to render. [Read TEH #003→]

🌱 In this issue of The Master Work, I sat with the question of whether the life underneath a business is one the body can actually live inside, and the crossroads of protecting the health of what you’re building versus the health of the person running it. [Read TMW #002 →]

🔮 Inbox Alchemy is the lab side of SPS—this is where the ideas across these letters get turned into practice through a sequential, evergreen email experience. [Join the lab →]

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply. Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread.

Leave a comment

💡The right idea finds the right person at the right time. You might be the one who gets it there.

🌿 The Understory

The Canopy gave you the four gauges. The Understory gives you the instrument you actually read, and the analysis you actually run from it.

For Stewards of The🌳Grove who have subscribed to The Understory: what’s waiting for you below is built directly from this week’s framework. What comes next is where this issue stops being a concept and starts being something you can actually run, on your own ecosystem, this week.

The Four-Gauge Console Read: a structured, single-touchpoint diagnostic that scores Connection, Consistency, Conversion, and Continuity from one reading, plus a Cockpit breakdown that tells you exactly which deeper framework to open next based on what you find. A full worked example, walked start to finish. Plus, a set of AI-assisted prompts built to run the read on your own content directly.

And at the close—a brief, honest note on how I used AI to write this issue, and what that process revealed about this week’s theme. This is a standing section in every Understory issue, because SPS’s position on AI is simple and steady: offload the mundane and repetitive so your irreplaceable creative energy stays where it belongs—in the work only you can do. These tools are built for exactly that.

Let’s get to work, Stewards.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →

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The conversation about AI has gotten very loud. So far, in that conversation aspects about the human experience in the AI era have gotten a bit lost.

The Event Horizon is a letter built for founders who want to look clearly at what AI is actually doing—to our work, our thinking, our identity, and our businesses—and lead from that clarity rather than react from the noise.

We’re not here to chase the tools or debate the hype. We’re here to figure out what it means to stay human while building something powerful—and to do that work together, week by week.

Human lead. AI empowered. Always.

Welcome back to The Event Horizon—where we look at what AI is becoming, what it’s asking of us, and what becomes possible when humans stay in the lead! 🌅

Substack integrated an AI-detection feature last week—without any advance notice or a prior warning period—and the internet split into its usual camps: necessary trust-check, or invasive overreach.

I want to stop here to say that I genuinely had no intention of joining this particular conversation. For one thing, I think if you follow or read me at all, you know where I stand on AI at the moment. And I do not intend on using the detection tool at all myself.

My mind works just fine, thank you.

Personally, however, I don’t think either camp is asking the more important question underneath this emotionally-charged roil. So I decided this week’s TEH will. This is the third turn in a thread I’ve been building since the inception of The Event Horizon, and it lands on what I believe is the real conversation we should be having instead.

Have we handed the machine the one job that was never its to do: deciding what’s good, valuable, and worth our attention?

📡 The Signal

As was thoroughly debated and commented on all over the platform this past week, Substack integrated Pangram’s AI detector (Pangram is a detection software company founded in 2023) as a new platform feature, July 21–23, alongside releasing Substack CEO Chris Best’s essay “Against Claudefishing.” on the platform’s publication, The Substack Post.

This is worth your time and attention because the platform named its values out loud, then immediately had to live with the cost of enforcing them through someone else’s tool. Even tech news, creators across LinkedIn and other social channels, and outlets like The Atlantic got in on the discussion.

Chris Best is right that the core problem is a mismatch between reader expectation and reality—when someone invests attention in text with “no human thought on the other end.” But Substack’s answer is a detector that estimates how many words a model likely wrote.

That’s a category error.

All writing is already intertextual and cumulative: built on prior ideas, sources, conversations, and other people’s work—filtered through the writer’s worldview, experience, and own intellectual rigor.

As I have been saying for a while now, the real question isn’t “Did AI write this?”; it’s “Did a human stand inside this work with judgment, care, and responsibility?”

By installing an AI detector, Substack has shifted the burden from authors and readers to algorithms. In doing so, its message to readers is, “Don’t fully trust your own sense of what’s valuable; run a scan and let an AI score mediate your judgment.”

That trains exactly the wrong muscle.

If the main human job in an AI‑saturated world is critical thinking and genuine discernment—the only real defense against slop, misinformation, and the misappropriation of technology—then we need tools and norms that strengthen that judgment, not outsource it to a black box that can’t see thought or care, only patterns.

And speaking of patterns, even those are not a reliable source for AI.

For one thing, when you take into account that many talented, integrity-driven, thoughtful content creators don’t speak the version of language the AI models were trained on, you add yet another barrier to the very human authenticity that we need more of, not less.

Neurodivergent, non-native English, and technical specialist writers are also more likely to be flagged as “AI-generated” simply for writing exactly as themselves.

This is the area where we admit that if English isn’t your first language, or your language at all, you are more than twice as likely to be penalized by an AI detector. (This is my shocked face. 😐)

This is not a bug, it is an entirely wrong premise to be guiding AI from, as the humans leading it.

Also, if you are someone who writes like they talk, but doesn’t necessarily have a librarian’s vocabulary, the “surprise” of your word choices will rank low, even though the content can be both valuable, and human written.

So, if a person is not a writer by nature, or stopped learning the craft of writing past a certain point, it can easily fall into a middle ground area that means nothing against the detector’s parameters, but happens to seem to overlap them.

If “average” is what the detectors are gauging their inspection on, and “average” is where a wrier’s level of writing happens to fall, do we really weed to be collectively adding one more layer of insecurity to an already incredibly hard and vulnerable craft.

I mean, we can’t all be Shakespeare or Dickens.

Best’s real goal, I think, was protecting the promise between writer and reader.

But the tool can’t do the one job it claims. That’s not a bug to patch—it’s a sign the question was wrong from the start.

So, what’s the right one?

🪨 First Principles

There’s No Outside Left to Check Against

AI text detectors didn’t appear out of nowhere; they’re the latest iteration in a longer line of automated text‑analysis and authorship‑attribution tools that evolved alongside increasingly capable language models.

Plagiarism detectors and basic authorship tools compared submitted text against large databases of existing documents (web pages, academic papers, student submissions) to find overlaps and copied passages. And they were fairly accurate at finding direct copying or lightly paraphrased text from known sources, but very poor at detecting original writing or text that didn’t exist in their databases.

And that particular flaw brings us to where we find ourselves now.

Those earlier tools were designed to detect source reuse, not generation by a model. And when LLMs started producing novel text that wasn’t copied from anywhere, plagiarism tools often saw it as “original” and thus not suspicious.

LLM-era detectors invert this supposition: they check resemblance to the model’s own training data. Which makes the detector both the suspect and the judge.

Why?

Because the training data is us—the entire corpus of human writing.

Hang on to that line because it will be important in The Human Layer, too.

So the external reference point that is supposed to keep the tool honest no longer exists. Verification has collapsed into a mirror of itself, creating a self-referential loop—forever serving up its own tail as evidence of its head.

The AI detectors based on the modern LLMs mirror the training data and we are the training data. They also are powerful pattern recognition machines. So, when it scans your writing, it compares you to the average pattern of all of its data—to the statistical average of everyone who came before you.

In effect, reducing your work to the average baseline of humanity.

That’s not comparing you to any fact, and it’s not a coincidental flaw—it’s the direct, structural consequence of detection losing its outside reference point.

We are all flattened out to occupy the mediocre middle where nothing interesting ever happens and nothing insightful, beautiful, or life-changing was ever produced.

As if that wasn’t bad enough, the math gets harder from here. If there is no outside reference—a genuinely unbiased database to reflect originality against—then knowing the origin was never going to be the right question.

The only coherent measurement is responsible synthesis vs. careless collage.

How’s that, you ask?

Synthesis is the deliberate transformation of prior material becoming something new through judgment.

Imagine a chef in the kitchen. They can take yesterday’s roast chicken, a handful of vegetables, and some stale bread and turn them into a completely new dish. The ingredients came from somewhere else; the meal exists because the chef knew what to keep, what to cut, what to combine, and what absolutely did not belong in the pan.

That’s synthesis.

Dumping the entire refrigerator contents into a blender is also recombination. That’s a science experiment with no judgment.

And then there’s a collage: a fragmented copy‑pasted from everywhere and nowhere, placed next to each other without being examined or integrated.

So now imagine someone “making dinner” by putting half a taco, two spoonfuls of cereal, a slice of lasagna, and somebody else’s potato salad on the same plate.

Everything is technically assembled. Nothing, however, has been considered. That’s collage: proximity pretending to be coherence.

And you know it just by looking at it.

Quality—what it is, and how we decide it— was always the actual question, and origin never changes whether something delivers on that.

As much as it pains me that we live in a society that operates predominantly on algorithms, we do. And that fact requires that I also address the governance of it in this equation. Algorithms already seemingly decide so much of what gets traction and visibility—that part is real.

“Yes, and…”

Visibility and value are two different jobs, and in this entire debate, rhetoric in both camps mistakes one for the other.

The algorithm is both overlord and tenant, in my estimation. As long as we remain in the equation, we create the environment for what the algorithm brings to the forefront, what gets visibility.

In a certain way, though, that is how we vote now. We decide what content gets traction and what doesn’t because we as a society, as a whole collective, decide what is valuable.

Only humans, after all, can decide what is good, valuable, and worthy of our attention.

When a reader doesn’t get value from the content in front of them, the move has always been simple—leave. That’s discernment, in real time, doing exactly the job it’s meant to and has always done—telling synthesis from collage.

All without needing a score from a skewed machine suffering from a quasi-Oedipal complex to do it for them.

If the verdict was never the machine’s to render, and never could be, what does it cost that we keep asking it to render one anyway?

🫀 The Human Layer

The Judgment We Can’t Afford To Keep Outsourcing

Though there has been extensive study on the topic, no objective “quality meter” exists.

Humans do, however, have a shared, evolved architecture for judging quality and value—but it’s not a clean, standardized benchmark. We’re human, so it’s a messy, context‑sensitive system that integrates attention, social cues, moral impressions, and personal goals.

That’s precisely why no algorithm can replace human judgment here: the “calculation” of worth is not just about the content; it’s about the relationship between a reader, a creator, and a moment in time.

The Human Calculus Of Worth

Decades of research in psychology, neuroscience, and consumer behavior points to the same conclusion: humans construct value in the act of attending, they don’t discover it pre‑written in the text.

That construction follows patterns.

What we pay attention to gets weighted more heavily in our overall judgment, so attention is not just a spotlight on value but part of the value calculation itself. We rely on extrinsic cues—author reputation, platform, design, social proof—to infer quality, often before we engage deeply with the substance.

And we care about character as much as outcome: trait impressions like honesty, curiosity, and care also influence how we evaluate a piece of writing in ways that raw usefulness cannot capture.

But these shared mechanisms do not add up to a universal scale.

Value judgments remain subjective, filtered through personal beliefs, experiences, and cultural norms. A single paragraph can feel profound to one reader and trivial to another, not because its “actual quality” changed, but because the relationship between reader, text, and moment did.

That’s why only humans can decide what is worthy of their attention.

An algorithm can estimate how many words a model likely wrote, but it cannot replicate the human calculus that integrates attention, social inference, moral intuition, and personal purpose into a single sense of worth.

Substack’s detector treats “AI‑generated” as the key boundary, when the real question readers are answering—consciously or not—is whether a human stood inside the work with judgment and care.

In a world that is actively debating the quality of everything—because AI now touches almost everything, cultivating that capacity and building the muscle for discernment and the calculus of worth is not a nice‑to‑have skill; it’s now the main human job.

There is a growing concern, as well as a growing body of research, around the effects on us stemming from the overreliance on AI. This overreliance measurably erodes confidence in independent reasoning and ownership of your own thoughts and ideas. Essentially the concern is that people are starting to treat AI outputs as final judgment instead of as inputs to their own human judgment.

If the calculus of worth is the muscle, the erosion isn’t hypothetical.

The Arc of Judgment

And as we discussed in the last issue of TEH, to complicate matters there’s a very real adoption-speed dissonance around the whole problem. 71% say AI is moving too fast, a minority trust it to decide anything—and we are adopting it faster than any technology before it anyway. Faster than computers, faster than the internet, faster than even social media, we are relying on a tool that the majority of people in this country don’t trust but still use every day.

In The Event Horizon #001, I asked you if oversight was collapsing judgment. And last issue, if you trust your gut over AI’s read. In this issue I’m asking who’s rendering the value verdict now, and whether we’re noticing that we’ve begun handing it over to the machine we need it most for.

For some time now, the capability has been around to input someone’s work into an AI detector and have it render an incomplete—and often incorrect—verdict. So that story really isn’t the debate at hand.

One of the ways that we exercise our human-only gifts is in deciding what’s valuable, deciding what is good and interesting and thought provoking and entertaining and inspiring and educational, …and what isn’t.

It’s always been that way.

It was that way long before these tools came into being, let alone started being used regularly. And as long as we remain cognizant and intentional, our human discernment will be the reason we stay in the lead of these machines, rather than be led by them.

🌅 Light on the Horizon

Synthesis, Out in the Open

Revoice

Cambridge University researchers built a wearable device, worn at the neck, that translates silent throat-muscle signals into fluent, emotionally expressive speech for stroke survivors living with dysarthria. Early trials show a 4.2% word-error rate and a 55% increase in how satisfied patients feel with their own ability to communicate. This is AI restoring someone’s voice, literally, rather than scoring whether it sounds human enough — the mirror image of everything this issue has been arguing against. Source

The Tree-Mapping Tool

USC researchers built a free tool using aerial imagery and AI to help cities find exactly where urban tree cover runs sparsest, giving planners an affordable way to target shade, cooling, and climate investment in the neighborhoods that need it most. An unglamorous, civic-scale example of AI doing exactly the repetitive, data-heavy work it’s suited for, so human judgment — where to actually plant, who to prioritize — stays with the people who understand the community. Feels right at home in a letter written for a community named after a grove. Source

Computer for Counsel

Perplexity built a legal-research AI where every answer has to trace back to a source a person can independently check, rather than asserting something unverifiable and hoping it holds up. It hands readers the receipts and lets them verify for themselves—the version of AI-assisted judgment this issue has been arguing for the whole way through. Source

🎯 Your Move

This week, run the calculus twice.

Before you publish something:

Could you explain, out loud, to a real person, why you chose this sentence over every other one available to you, and what you cut to get here?

And before you decide something you’re reading isn’t worth your time: is this my own judgment talking, or a score I haven’t checked against it first?

📡 Signal Back

This week’s question:

Where have you caught yourself outsourcing a value call—deciding something is good, worth your attention, worth your trust—to a score, a scan, or a system, instead of making the call yourself?

Tell me where you noticed it, and what you did next.

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply. Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread.

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

💌 A Note on How I Used AI to Write This Issue

Usually this is an extra section in The Understory: a brief, honest note on how I use AI to write the issue. This is a standing section in every The Event Horizon Understory, because my position on AI is simple and steady: use the tools, stay in the loop—stay in the lead.

But for this specific issue, and this particular argument, I decided its transparency needed to be above the threshold this week.

This issue almost didn’t survive its own thesis.

I sat down to argue that judgment can’t be outsourced to a score, then spent the better part of a week working the argument through with my favorite AI tools.

I started with voice notes I transcribed—a rant on what I thought about this whole debate.

Then, after I pulled the individual ideas out of those notes, I took them to Perplexity to research 26 different aspects of my argument, find and read sources, reflect on it all, and ask another query based on the insight it gave me. Back and forth for 26 full turns.

During this process, I also turned to my Claude Cowork. It helped me map out article sections and beats, pressure-test the throughline, and even catch a factual error in my own research brief (Perplexity’s tool is Computer for Counsel, not Computer for Council—small, but this issue argues accuracy matters, so it mattered here too).

None of that structure wrote the argument for me.

The category error, the synthesis-versus-collage distinction, the decision to build this issue around discernment instead of detection—that’s all mine, argued out loud– literally and figuratively, revised more than once because an early draft wasn’t precisely what I was trying to say.

What these tools give me is a second set of eyes fast enough to keep up with how quickly I think—even when I am tripping over my thoughts to get them lined up, a way to do research that would have otherwise easily taken me a couple weeks so I could zero in on what I specifically wanted to shine light on, and a place to catch my own mistakes before you have to.

That’s the whole case this issue is making, really: stay the one who’s accountable for what the tool hands back, and what to do with it, every time.

🌿 The Understory

What you just read is the argument. What follows is the work.

What comes next was built for The Understory—and it goes where this issue has been pointing all along: into the practice of proving, to yourself first, that your own judgment is still doing the work I just spent this issue arguing only it can do.

This week’s Understory is two things. A Practice Protocol: “The Rejection Ledger“—a simple, ongoing habit of logging every AI-generated line, idea, or draft you deliberately cut, and why, so the discipline behind your discernment has a record even when nobody’s asking to see it. And a Research Deep Dive—the full annotated source set behind this issue’s argument: the bias data, the detector-evolution research, and the studies on what AI overreliance actually costs your independent reasoning, for anyone who wants the receipts behind the case.

Let’s get to work.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →

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The Master Work is a Sunday letter for founders who have always known: the wider you draw from life, the deeper you can build. The muse of inspiration—and the source of wisdom—comes from the menageries of Life. The songs, the stories, the essays, the experiences, the conversations, the contemplation—all of it feeds the mission. Everything you’re reading, hearing, noticing, and living belongs here, in The Master Work.

“Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth and our freedom.”

—Viktor Frankl

Welcome back to The Master Work!

This week, the thread found me in the gap between who I know I am meant to be and who I was actually being for about the larger part of this past week. This month, I’d been deliberately building a habit meant to shape me into someone steadier, more myself, and more recognizable in the mirror—and then life did what life sometimes does, and the habit-building fell away almost entirely. Three days of skipped better food choices, skipped reflections, skipped “all the things” except muddling through what was in front of me the best I could.

I’ve been sitting with a question underneath all of it: does becoming who I actually want to be require some kind of unbroken streak, or is it something else entirely?

Turns out, when I went looking for answers, guidance, and perspective—in a decades-old psychologist’s memoir, in a movie I’ve seen a dozen times, in a Robert Frost poem I hadn’t read in years—the answers, as they often do, showed up everywhere… and they all revealed it was always about the return, and the grace you give yourself on the journey back.

So that’s this week’s thread: the choices we make on purpose, the habits those choices eventually become, and the grace and grit required to keep finding our way back to that path every single time we wander off it. Which, if you’re anything like me, is simply called your life.

🌱 The Seed

Every habit was born as a choice, and swaddled in a cycle of repetition until it stopped being recognized as one. I spent the last few days relearning that the hard way, which is really just a nicer way of saying I fell off something I’d only just started building. I found out exactly what happens when the choosing subtly stops without your permission—and what it takes to notice, and come back.

Conscious founders are often the ones most practiced at choosing on purpose—and also the hardest on themselves when the habit breaks or falters in its formation.

Why is that?

I think it has everything to do with the fact that if identity really is built habit by habit, then who you’re becoming as a founder and who you’re becoming as a person are the same project, which raises the stakes on both sides.

What we’re looking at together this week then is this: the actual mechanics of how a choice becomes a habit—whether they are personal or professional (and how slow and unglamorous that process really is), and the harder, less-discussed half of it—which is what it means when you fall off, and whether that’s failure or just the shape the path was always going to take.

Because the real question was never whether we’d walk this path without ever stepping off it. It was what we’d do the moment we noticed we had.

📖 The Work

First things first, because I can hear the guy in the back asking the question: I make choices every day, and I know what they are. I control what I do and don’t do. What if I don’t want to play the habits game?

We’ll get to why you don’t have a choice to opt-out (see what I did there?), because biology ensures every player has a turn, whether they understand the rules or not.

First, the bit about those choices you think you make every day, and the ones you actually do, mix with a little brain science (just a little, I promise).

Choices and habits are on opposite ends of the same behavioral spectrum.

Choices are the deliberate, live decisions made in real time, that require your attention and agency in the moment it happens. They are a single event that engages the brain’s prefrontal cortex (PFC). This is the area of the brain that houses the skills that let you plan, focus, and control impulses. It’s also where your ability to make thoughtful choices instead of just reacting in a given moment comes from.

If you went out for a hike, a choice is like stepping onto a path before you know whether it will feel familiar. You see a cairn you’d like to get a better look at, so you make the decision to move toward it, and evaluate the terrain as you go. You notice the rocky areas, the trees, the animal noises, the view, the ground beneath your feet—you are consciously taking it all in.

Habits, on the other hand, are automatic, require absolutely no forethought in the moment they are executed on, and are performed mostly below the conscious level. These completely outsourced activities originated in decisions you made long before they are put into practice.

A habit is a choice repeated until it stops feeling like one, and now the brain files it the basal ganglia portion, where it can run the routine without much further input from the PFC. This pathway functions especially well when you are under stress, tired, or distracted.

So, let’s say that trail from above is one you are very comfortable with because you’ve traveled it before. This time, because you know what to expect, you are lost in thought while you travel the trail. You don’t see the rocky areas, or hear the animal noises or wind through the trees, or really see much of the view. And the ground underneath your feet, while you rationally understand that it’s there, you don’t really feel it anymore either.

With me so far?

Okay, so the third mechanism in this continuum pathway is the intersection of the two above scenarios—the moment when you make a conscious choice and the moment a habit kicks in an automatic routine—and it is a moment of time where a choice is being repeated but hasn’t yet become automatic. So you’re still aware and thinking about what you’re doing, but each time that trail is traveled by you, the threshold to becoming an automatic function shrinks.

At this point, while you are still actively making the choice, you are voting for the identity this behavior leads to—and is represented by—with every rep.

For the record, that threshold doesn’t shrink on any tidy schedule. The oft-repeated ‘21 days to build a habit’ line is a myth—the actual research (Phillippa Lally’s habit study out of UCL) found it can take anywhere from 18 to 254 days (yes, the better part of a year!), depending on the person and the behavior.

So if it’s been a while and it still doesn’t feel automatic yet, that’s not a personal failure. Give yourself the grace to allow that’s just how long the trail actually takes to wear in.

After that choice is ingrained enough to become a habit, though, it moves into the realm of systems that run on autopilot, preserving your precious brain energy for new choices and decisions. When this happens, something else you don’t even recognize also kicks in.

The volume of choices you even perceive as a choice recedes.

Why?

Because the brain is evolved to do two things really well… Besides keeping you safe, your brain also always wants to keep you “right.” This is the identity component. In order to maintain who you have shown you believe yourself to be, the choices you are aware of are narrowed to what is in alignment with that identity.

So if choices outside the arena of what you have confirmed through your previous choices (i.e. your habits), they don’t even appear on your radar.

This is why it can be so damn difficult and frustrating to change established habits. It’s also why it can appear that other people are superhuman in their willpower. What’s actually happening is they’ve established enough habits that, in effect, rule out contradictory behavior from the equation for them.

It’s a reinforcing loop. And it works both ways.

Because habits don’t only result from your choices. They also eventually filter which choices even show up in your perception of the world at all.

I’m sorry to say, kind “I’m in control” sir, that every habit begins as a choice, both the good ones and the not-so-good ones, and even the ones you don’t think you have.

Now, staying rooted in who you’re wanting to become isn’t about never stepping off that path. It’s about what you do the moment you notice you have.

Take my week of falling (head-first, in a muddy puddle, and twisting my proverbial ankle) off the path of my new intentional habit-building exercise. After a few days of feeling aggravated at the surface-level of my situation, I finally realized that my bigger agitation was the sacrifice of my new ritual, and the not really noticing I had.

Until I did.

And after some internal scornful scolding, and a little reflection—followed by the grace necessary to move past what felt like failure and reach for something bigger, I also recognized that part of the solution to one was, in fact, getting back on the path of the other.

So how do you get a new choice off the ground when the whole mechanism is built to protect what’s already established?

BJ Fogg’s research at Stanford has an answer: shrink the behavior down until the ‘choice’ barrier basically disappears, then let repetition do the rest. Don’t depend on motivation. Don’t rely on willpower. Just make the next choice so small it barely registers as one.

Becoming, then, was never going to be a straight line—no matter how badly the productivity content out there wants it to be.

Instead, it was inevitably going to be a spiral: choice, lapse, return, choose again, with deeper alignment this time. In the movie Groundhog Day, Phil Connors doesn’t get out of the loop he feels stuck in by finally having a good day. He gets out by choosing, on purpose, inside the same repeated day, until the choosing itself became who he was.

The loop never changed. He did.

As you’ll see in The Mosaic, Robert Frost knew something similar—even if we’ve all been misreading him for a century. His narrator doesn’t claim the road he took was better—he says that choosing it, and living inside that choice long enough to look back on it, is what made it matter at all. Not the diverging paths. The return trip, walked over and over, until one choice becomes solid enough to call a Self.

That’s the spiral, then: not an unbroken line of good choices, but choice, lapse, and the return, traveled enough times that the return itself becomes the thing you can count on. Somewhere in all that repetition, the trail wears in—and it’s not because you never stepped off it.

It’s because you kept coming back to it anyway.

😊 Happiness Habit of the Week

This week’s habit:

The next time you notice you’ve drifted off a path that matters to you, skip the grand comeback. They’re never as fulfilling as they appear to be, anyway.

Instead, shrink the return down to its smallest possible size—one page instead of the chapter, one walk around the block instead of the full route, one breath instead of the whole practice.

And if you are what you repeatedly do, then following this minimum viable solution path is the strongest step to stay on the trail to your desired destination.

It still counts as a vote.

Let it be enough.

🎨 The Mosaic

We’ve arrived at the living proof of both the issue’s premise, and the edition’s: A life lived wider allows the human at the center of it to build deeper.

The mosaic is a collection of sources relating to the issue’s topic, that you can add to your menagerie, and widen your life, if you choose.

I didn’t go looking for these five things this week—most of them found me, as they so often do, through the genuinely mysterious magic of the universe… They found me mid-survival-mode, still trying to show up as I know myself to be, while also trying to give myself grace for the fact that I am human.

A psychologist’s memoir I return to more and more, even if just to read a passage or two, like a tome—only portable, and with flags and scribbles. A movie I’ve watched probably fifteen times and somehow still needed again (plus, I mean, come on… When you need a laugh, Bill Murray is one of your guys, right?!). A poem I fell in love with in high school English class, and yet haven’t read in many years. A song that’s been on repeat without my full permission (though I notice I can’t help but smile when I hear the whistles at the beginning). And an article I found that named, in plain language, something I’d been circling around for months.

Here’s the thread running through all five—and why each one earned its place this week.

The Five Sources:

📚 Man’s Search for Meaning—Viktor Frankl

Frankl wrote this from inside a concentration camp, which is worth remembering every time habit advice starts to feel abstract. His argument isn’t really about habits at all—it’s about the one freedom that can’t be taken: the space between what happens to you and what you do next.

That same space is where every habit gets built, one choice at a time, and it’s also where the return happens every time you’ve drifted from the path you meant to walk.

📜 The Road Not Taken—Robert Frost

Frost’s narrator never claims the road he took was better—only that choosing what he did, and living inside that choice, is what made all the difference.

That’s a truer read of habit and identity than most of what gets built into productivity content: the road doesn’t have to be perfect. It just has to be walked, returned to, and walked again.

🎵 Home—Edward Sharpe & The Magnetic Zeros

“Home is wherever I’m with you” is the line everyone remembers (and it’s a truly lovely one), but the quieter argument in this song is that home isn’t a place you arrive at once—it’s built through returning to the same person, the same practice, the same choice, again and again until it holds you.

That’s what staying rooted actually feels like: less a fixed location (hello location independent lifestyle! 👋🏻), more a repeated act of coming back.

🎬 Groundhog Day (1993)

I’ve watched this one probably fifteen times, and it wasn’t the redemption arc I came back for this week—it was the boredom.

The long middle stretch where Phil is just doing the same day, badly, over and over, before anything shifts.

That’s the part that never makes it into the ‘choose your habits’ conversation: the repetition is genuinely tedious before it’s transformative. Nobody tells you the loop feels like nothing’s happening, right up until it’s everything.

📰 “Why the Most Important Decisions of 2026 Aren’t Your Goals”—Psychology Today

This one put language to something I’d personally been sensing all year: the real leverage in a life isn’t the big declared goal, it’s the small, repeated, often invisible choice made underneath it.

The goal is the destination. The choice, repeated daily, is the actual vehicle—and it’s also the thing that’s easiest to quietly abandon when no one, including you, is watching closely.

🌿 What’s Taking Root

This week, I’m sitting with the fact that becoming who I want to be was never going to be a straight line (obviously), and that every time I’ve stepped off the path this, or any, year wasn’t proof I’m failing at it—it was just the shape the path actually takes, with all its twists and turns.

So here’s what I’m carrying forward: the return itself, however small, however many times it takes, counts as progress. All forward momentum does.

Maybe you’re circling something similar.

Where have you had to find your way back to yourself lately—and did you give yourself credit for it?

📬 Write Back

Where have you had to find your way back to a version of yourself you’d stepped away from?

Join the conversation—I’d love to hear what the return actually looked like for you, not just the falling off.

One honest line is enough. I read every reply.

Leave a comment

If this issue resonated with you, I have three small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name, and a community, for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

💡The right idea finds the right person at the right time. You might be the one who gets it there.

✨ Here’s to every version of you the return brought back home.

To everything that feeds you,
~StacyLynn
Founder, Sitting Pretty Strategies
Root Deeply. 🌳 Rise Differently.

P.S.

And if you know a founder who’s been silently hard on themselves for falling off a path they’re actually still walking—send this their way.

Share

Sometimes grace finds you through someone else’s hands. 💌

The conversation about AI has gotten very loud. So far, in that conversation aspects about the human experience in the AI era have gotten a bit lost.

The Event Horizon is a letter built for founders who want to look clearly at what AI is actually doing—to our work, our thinking, our identity, and our businesses—and lead from that clarity rather than react from the noise.

We’re not here to chase the tools or debate the hype. We’re here to figure out what it means to stay human while building something powerful—and to do that work together, week by week.

Human lead. AI empowered. Always.

Welcome back to The Event Horizon—where we look at what AI is becoming, what it’s asking of us, and what becomes possible when humans stay in the lead! 🌅

The same day I had a woman tell me how she has an AI that keeps on top of her grocery shopping for her (it knows the ongoing list, keeps track of what she’s using and not using, loads her online cart, makes the purchase for delivery after her review and approval 🤯), I read research showing that more than three-quarters of Americans—a whopping 82%, in fact—responded to unrelated studies about AI recently expressing that they feel forced to choose between a fast decision and an informed one, while simultaneously saying they doesn’t trust AI-made decisions.

So this week starts with a number that’s hard to look away from:

71% of Americans now believe AI is moving too fast, and only 18% would trust it to make a decision for them. This is according to an Economist/YouGov poll released in May.

(Can you guess which camp I’m in? Stay tuned, and it will become clear.)

I think those numbers are the visible half of a story that’s also unfolding inside the founders building with AI every day—the same erosion, showing up in two places at once.

I’d like to talk a bit more about that, so let’s see where the signal leads…

📡 The Signal

So what’s happening in the AI landscape right now?

Almost three-quarters of the population of the US say AI advancement is moving too fast, and 81% don’t trust it to make decisions for them. At the same time, despite this overwhelmingly anxious sentiment, Americans are using AI more and more in their daily lives.

The use of AI search and chatbots (think Google Gemini, ChatGPT, Copilot, and Claude) alone is up—49% of Americans use them every day, according to Pew Research Center—while the overall outlook on AI remains quite pessimistic. As I write this letter, more Americans than not expect AI will have a wholly negative impact on society, and particularly on the economy, in the long-term.

The pattern, though counter-intuitive, is clear:

Adoption is rising fast
AND
Trust and optimism are declining faster still.

In a Computer Weekly article from February of this year, the signs were already starting to show. A quote from Tyler Johnston, executive director of The Midas Project (an AI watchdog group), shares the predominant perspective:

“It’s not clear if [AI] technology will, on net, benefit everyday people or disempower them.”

All this and the technology is still in its infancy, really… But isn’t that how it’s always been? A new technology comes into our reality, turns everything on its head, and people swear it’s the end of the world as we know it. And usually they’re right. But more on that in a bit.

What the data is actually revealing isn’t outright rejection of AI’s capability, but rather the public at large is rejecting something else entirely.

As Brian Solis observed in his article on Medium in June, “The AI Backlash is Really a Crisis of Trust and Agency,” “ the revolt against AI isn’t just fear of technology. It’s fear of losing agency.” And I believe he’s right. People aren’t afraid of the technology in and of itself as much as they are afraid of a future they feel they can’t choose for themselves.

Because the conversation around AI is no longer just about the technology and its advancement. It’s also about how it advances, who decides that, and what that will mean for society at large.

For months, I’ve been saying to anyone who will talk with me about it that while the possibilities of AI are truly staggering to think about, the technology is moving so fast that individuals, particular segments and communities, as well as whole industries, will find that they cannot process the changes happening in real time, nevermind fast enough to feel like they have any control.

And Solis addresses this as well, saying “ When people feel they have no voice in the systems reshaping their lives, resistance becomes rational.”

So here’s a question for the founders reading this, and thinking, “Hey, I use AI every day. I got this. No problem.” ...if your audience is part of that 71% who distrust AI and by default AI-driven companies, how will their relationship to your “AI-powered” language change? Because it likely already has, whether you’ve noticed or not.

Here’s a stronger question, and one that includes you in its scope: if the backlash surrounding AI isn’t really about capability, what is it about—and what does that mean for what a founder actually needs to protect?

🪨 First Principles

The Backlash Is a Referendum on Consent

My personal position, and in turn the position of SPS, has always been that AI belongs working on the mundane and repetitive so human energy stays where it’s irreplaceable—the creative, strategic, relational work only a founder can do. The data in this week’s issue turns that principle from an integrity stance into a competitive market mechanism: visible human presence has become the offer’s actual credibility layer, the thing the market is now pricing, whether founders have realized it or not.

The stakes intensify when you add the distinction between analysis and judgment.

AI performs analysis extraordinarily well—it is a pattern recognition machine, literally. But judgment is a different kind of thing altogether. It integrates context, values, relationships, history, and consequence, and those collectively fall into distinctly human capacity territory.

And this is the exact fault line the backlash discussed above traces.

A founder who lets AI absorb judgment-adjacent decisions, even efficiently, even with good intentions, is handing over precisely the thing the public has just said it doesn’t trust anyone to hand over invisibly. The backlash and booing (did you see that video?) of AI names something specific—people resisting being decided-about without their knowledge or consent.

This pervasive stance reframes what a founder needs to protect.

It was never really about how much AI a founder uses. It’s about how visible and consensual the judgment layer stays for the people on the other end of it. This is where genuine discernment and the expertise built by standing in the work long enough to know its terrain live.

Trust is a scarce resource, and an infrastructure built on top of judgment, context, and insight that only your experience can provide is the competitive moat, as well as the strategic differentiator, for any founder looking to scale and sustain in the next decade and beyond.

I would argue that’s always been the only moat that matters—and maintains. AI has simply sharpened that understanding.

And here’s the harder part—the test the public is applying to founders from the outside is exactly what’s being eroded by founders on the inside, one deferred decision at a time.

🫀 The Human Layer

The Same Test, Applied From the Inside

In the last issue of The Event Horizon, we talked about cognitive sovereignty—the idea that people should retain control over their own thinking—their attention, judgement, memory, and decisions—and avoid environments that would steal and steer it manipulatively or coercively.

But what happens when that environment is your own internal landscape, and you find yourself second-guessing your own thoughts and conclusions because an AI says something different?

That’s the layer of this trust and agency dilemma that’s not getting talked about much yet. There is, however, a growing body of research on leaders who rely on AI for decision-making, and one definitive pattern is emerging: the more people defer to AI’s read of the terrain, the more they begin to doubt their own, even when they know they disagree with the machine’s output.

Without protecting what only a human can do—the very discernment you earned through years of building your expertise and banking your experience, this dilemma becomes so much bigger than just about making a values compromise.

It means you are actively building on ground that’s actively eroding right out from under you. As discussed above, AI is a pattern recognition analyzing machine. And in the absence of your patterns (discernment and insight) to analyze, it will pull data from somewhere else it can (the aggregate) to create the necessary output. It doesn’t care. It can’t because it’s not human. It only operates to get whatever job it’s been given done.

Remember that line a few minutes ago—that when a new technology turns everything on its head, the people who call it the end of the world as they know it are usually right? Let’s shift gears for a moment (bear with me, I promise it will be worth the payoff).

I want to tell you a little story.

It’s the early 1900’s and you are a professional horse and buggy driver. Picture it: you’ve spent your entire life mastering this mode of transportation. You know how to read a horse’s mood, mend a wheel, judge exactly how far you can travel before your horse needs water. You’ve spent your savings on the best rig money can buy.

Then the automobile arrives.

For a while, roads hold both—your horse and buggy right alongside cars, each convinced the other is the passing fad. And for a while, you’re right. You get where you’re going, and a certain percentage of society still relies on you for getting where they need to go, same as always.

Then the world starts to change right before your very eyes.

The roads get repaved for tires instead of hooves. Gas stations replace feeding troughs. Insurance, traffic law, city planning—all of it rewritten for cars. The world’s infrastructure moves, and the driver who was right for years—you—is suddenly standing on ground that no longer holds you up. Your skill is exactly as sharp as it’s ever been, and the ground simply changed shape underneath it.

That’s the moment worth sitting inside right now.

AI is the new roads being poured under our feet: what clients expect, what “good” looks like, what a competitor can now accomplish, all shifting at once.

And it’s fair to grieve the expertise built for terrain that’s disappearing, even while you build for the terrain replacing it.

So how do you marry the deep body of expertise and experience that you have amassed over the years with the sheer power of this world-changing technology?

First of all, know that what made you great at your job for years, is still what separates you from the crowd now. Just with some slight upgrades to your task and execution. What shapes AI to your will rather than being led by it is operational expertise that you can give to the AI so its filter is your judgement. This requires you to codify your judgment.

What does it mean to codify your judgment?

Putting your expertise and systematized decision-making frameworks in an instructional form AI can read, use, and craft output through. This is how the founders who are using AI and winning are in fact getting those results. This is the edge you need over AI—to stay present, to stay the discerning factor of your AI’s output, and to stay the human in the lead of this revolutionary moment.

Years in the work aren’t a sunk cost—they’re the raw material of the discernment this moment actually requires. The moat was always judgment, not task execution, and it stays intact only if you apply it to guide AI rather than defer to it or refuse it outright.

🌅 Light on the Horizon

The Same Test, Passed

A Vaccine Component Designed by AI Just Completed Its First Human Trials

Researchers at the University of Cambridge used AI to design a vaccine component that has now completed its initial human trials—an early, uncertain stage of medical discovery that AI is uniquely built to help with.

It processed a scale of molecular possibility no human research team could hold in their heads, while the clinical judgment, trial design, and safety oversight stayed exactly where they belonged: in human hands.

This is the “AI on pattern recognition, human on judgment” argument playing out at its highest possible stakes—AI expanding what’s discoverable while the decisions that actually matter to a patient’s safety never left human hands. Source

Struggle First, Consult Second: What New Research Says About AI and Critical Thinking

New research covered by Science News found something worth reflecting on: people who worked through a problem on their own before consulting an AI chatbot performed measurably better on critical thinking tasks than those who consulted the chatbot first.

The sequence of when you consult AI relative to your own effort appears to determine whether it sharpens your thinking or dulls it.

It’s the research behind this issue’s Understory practice, “Before You Defer”—so if you want the evidence before you try the tool, this is where it lives. Source

Small Business Owners Are Using AI to Extend Their Expertise

2026 reporting on small business owners found the same pattern playing out at ordinary scale: owners using AI tools like ad variant testing and cash-flow summaries to extend genuine expertise, compressing a week of routine work into an afternoon so their actual specialty gets more attention.

No enterprise-scale drama here, just a workable version of “codify your judgment,” happening at exactly the scale most of us are building at. Source

🎯 Your Move

Pick one place in your business right now where AI is producing something on your behalf—an email sequence, a proposal, a piece of content.

Now, ask yourself plainly: is my judgment actually shaping this, or has the machine started making the call on its own?

Name one thing only your years of experience would know to include, and make sure it’s there before that piece goes out. That’s part of the “codifying your judgment” process, and what it looks like in practice: one decision, reclaimed, this week.

🌀 The Ecosystem Pulse

Before you go—one honest question: Did this issue nourish something?

Reply with a single letter and let me know how this one feels for you:

A—Yes, I feel more aligned
B—I’m still sitting with it
C—This one didn’t connect

Every signal strengthens the ecosystem. Thank you for being part of it.

🧭 Constellation Compass

🌌Elsewhere in the Sitting Pretty Strategies constellation recently:

🗺️In this issue of Elegant Email Ecosystems, I made the case that voice and visibility multiply rather than add—and introduced the Four Anchors framework for locating the note before you worry about the room. [Read E3 #028 →]

📍In this issue of Pretty Strategic, I made the structural case for coherence as an AI-era indexing requirement—and why the founders who built foundation-first are the only ones a recommendation engine can surface with confidence. [Read PS #039 here ➔]

🌱 In the inaugural issue of The Master Work, I sat with the question of whether the life underneath a business is one the body can actually live inside, and the crossroads of protecting the health of what you’re building versus the health of the person running it. [Read TMW #001 →]

🔮 Inbox Alchemy is the lab side of SPS—this is where the ideas across these letters get turned into practice through a sequential, evergreen email experience. [Join the lab →]

📡 Signal Back

This week’s question:

When was the last time AI’s read on something didn’t match your gut—and what did you do with that gap?

I’d genuinely love to hear where you landed, and what it told you about how much room you’re currently giving your own judgment.

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply. Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread.

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

🌿 The Understory

What you just read is the argument. What follows is the work.

What comes next was built for The Understory—and it goes where this issue has been pointing all along: into the practice of protecting the judgment you just spent this issue making the case for.

This week’s Understory is two things. A Self-Assessment: “Where Are You on the Spectrum?”—a short calibration tool for locating yourself somewhere between full deference and full resistance, so you know exactly where your own tuning currently sits. And a Practice Protocol: “Before You Defer”—one concrete pause-and-check practice for the moment AI’s read disagrees with your own, so the years you’ve spent building your judgment keep doing their job.

And at the close—a brief, honest note on how I used AI to write this issue. This is a standing section in every The Event Horizon Understory, because my position on AI is simple and steady: use the tools, stay in the loop—stay in the lead. Those ideas are not in conflict, and The Understory is built on exactly that premise.

Let’s get to work.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →

Read more

Welcome back to Elegant Email Ecosystems, where we believe being found and being heard are not the same thing, and only one of them is going to build the kind of trust your business is actually after.

Last week, we talked about the AI systems already reading your business before any human decides to trust you. This week, we’re looking at what those humans are reading when they come across your business—and whether it’s actually your voice they’re hearing, or just the noise standing in for it.

There’s always such a rush to “get seen,” to post more, to “be everywhere.” Yet even with those efforts, founders who follow this path often find themselves with one of three problems:

⓵ They are scattered across platforms like buckshot, with a topic here, a half-crafted message there, some opinions over on that one, and nothing really connecting the lot.
⓶ A honed single platform, with only a few really well thought-out pieces, and only a small number of views or reactions
⓷ Both 1 and 2 , and the frustration that comes from incoherence across a brand, and knowing you are the bottleneck, but not knowing how to fix it.

Often under these circumstances, founders will turn to the thing they can view as “success” or “scale”: their perceived visibility. They decide their voice will come, if only they keep posting enough, and can just get enough views, likes, and comments going for the algorithm’s liking.

While the visibility piece is part of it, and the voice piece does take a bit to cultivate, the overall equation—and the roles each component plays—is not really well orchestrated by a brand struggling to find their cohesion and therefore root their place in the landscape.

So which is it: are you missing the voice, the visibility, or the orchestration between the two?

Solving For Only Half the Equation

There’s a version of the visibility conversation that most founders have heard so many times it’s practically woven into their operating assumptions.

Show up. Post consistently. Be everywhere your audience is. Build your presence. Stay top of mind.

The logic is that if you’re visible enough, for long enough, to enough people—something will click. Trust will follow. Clients will come. The algorithm will eventually reward the consistency.

Founders and creators treat visibility like the end-all-be-all of growth and scaling because of this pervasive ideology.

And when things aren’t adding up to what they think they should be, they believe it’s a volume problem to be solved by a volume gambit—more eyeballs, across more platforms, more often—and everything will work itself out. The thought is that it’s just a numbers game.

This response to visibility, by the “more” definition, is trackable by metrics that are easy to access, and so it often evolves into the goal itself, rather than be a tool in service of the actual goal.

And there is something true in the pursuit of this idea.

You cannot build a business in silence. You can’t connect with people who have no idea you exist. Visibility does matter. Getting your work in front of the right people is real, non-negotiable, deeply important work.

And also...

The name of the game—the actual goal—is in fact to get your core message, articulated in your voice, in front of the only people it is meant for, in language they will recognize as their own.

The role of visibility in the equation of reach is in practice much narrower than the acclaim and notoriety that has been bestowed upon it. In the simplest terms, visibility’s only job is to act as a threshold condition. On its own, visibility is not a growth engine, despite so many founders operating from the assumption that it is, nor does it create any real trust, nevermind conversions.

Don’t believe me?

Think about this (and see if you are described in this scenario):
A person can see, and scroll right past, the same brand fifty times and still not ever buy, refer, or even remember the brand’s message accurately.

I’ve done it, and I’m sure you have, as well.

What visibility actually does for a brand is prime pattern recognition: it determines whether you’re already “known” enough to come to mind explicitly in the exact moment that a person needs what you offer.

The visibility component of the reach equation opens the possibility for that recognition.

But then what? What is the other component in the reach equation?

When you’re not getting the feedback and results you’d like from all that posting and scattershot, it’s worth asking, “what is my visibility actually carrying?“

Because voice and visibility are not the same thing. Most founders are chasing one and calling it the other, never realizing they are two factors in the equation they are trying to solve for.

Visibility is your outward effect. It’s how far your signal travels. Voice is your upward frequency. It’s the signal itself. And you need both, aligned and working together, to get the reach—and the prospects, clients, and customers—your business needs to thrive and scale.

🎙️Real Founder Confessions

I Thought Writing More Would Find My Voice for Me

When I first started Sitting Pretty Strategies and this whole marketing online journey, I had the idea that if I just kept writing about things I was thinking about that I would “find” my voice—or more accurately, I thought that it would find me.

I think that’s a pretty common misconception, actually.

I instinctively understood that my particular messaging would be what differentiated me from the many other possibilities in the marketplace. And that meant crafting it from a voice that was uniquely mine.

From this perspective I wrote, and I wrote, and I created content like bunnies, well, we all know what the bunnies do. …I felt like a machine churning out content that never really connected with my intended audience, though. And after a while, I ended up in a cycle of high output, followed by a phase of confusion on why nothing was working, and then “taking a break” to figure things out. But this only ever led to having to start the whole thing over and over again.

The problem was that I didn’t know what was missing.

With that “just keep writing” perception, my output was kind of all over the place. One piece would sound like a college professor teaching a lecture. Then I’d send an email that was so conversational it could have been to my best friend. After that I’d put up a social post that sounded like I’d spent four years on my alma mater’s pep squad (I did not, though I am a good cheerleader of someone else’s journey). And I would finish that week with another piece that was meant to be thought leadership, and probably came across more like I was making a case in court.

You get my meaning… Have you ever felt like your content shows all the different sides of you, but never the face you’re wanting to show to your ideal audience?

We are all more than one thing, and your brand’s voice will always reflect more than one part of its author. But the element that separates a business that floats along from one idea to another and never really connects and brands that build strong communities and thriving businesses can be summed up in one word: resonance.

Resonance is that goosebump moment when someone reads your content or your story and says, “Oh wow… they get me.” That’s what marketing is meant to do. And it’s what brands who have success have in spades.

And resonance requires two ingredients to coalesce:

Voice and Visibility.

And while this game is still something I am figuring out as I go and grow, I know now that I was missing clarity on both counts for a long time. Until I recognized this formula, and how both components rely on one another to make your marketing work well, creating content was mostly frustrating and exhausting.

Most founders have more visibility than they have resonance, because they haven’t done the voice work necessary to balance out the equation. One without the other leaves you working harder than you should have to, for less than you deserve to get.

Neither One Works Well Alone

Most founders don’t have to choose which half to fix first because the truth is neither half of the reach equation was ever optional. And neither one can cover for the other no matter how well that component is built.

Here’s what happens when each one tries to carry the whole load by itself.

The Whisper Problem

Voice is the meaning-making layer of a brand.

It tells people what you believe, how you think, and why your approach is different. This is what helps audiences trust you instead of just noticing you or scrolling on by.

However, a clear, resonant, deeply specific voice that no one can find is a song being played in a room with the door closed.

The founder who has done the real work of knowing exactly who they are, what they believe, and what makes their approach distinct—but who never consistently shows up somewhere their audience can encounter them—is carrying a gift their audience never gets to receive.

The voice is there. The visibility isn’t. And the people who would be transformed by the work simply never find it.

The Noise Problem

Visibility is the distribution layer.

It gets your ideas in front of more people through content, appearances, partnerships, search, social, media, or community presence—increasing the chance that the right people encounter your brand repeatedly. It’s that repetition that breeds familiarity with those people, and is often what turns a spark of awareness into consideration.

High visibility without a clear voice gets you a different kind of problem, because visibility on its own is not enough.

Sure, you’re in front of a lot of people. But what they encounter when they land on your content doesn’t connect. It’s out there, and it may even be good. Without a clear, resonant voice to carry it, though, it’s generic enough to be forgettable. It feels like it could have been written by anyone in your niche—or by AI without adult supervision.

So, they don’t stay. They don’t return. And they don’t send their people to you, because if they try to explain what makes you different, they come up empty. The referral they could have given you gets replaced by a shrug.

What It Looks Like When Only One Half The Equation Is Working

Play the Whisper Problem or the Noise Problem out long enough, and they land in the same place: real effort expended, and still unheard.

Here’s what that actually looks like in the day-to-day work of running a business:

When a Voice Has No Room to Land

• A message so clear in your head it never makes it fully onto the page

• One newsletter, sent to eleven people, three months apart

• A brand statement recited perfectly on discovery calls and never once published anywhere

• Content living permanently in drafts folders, waiting for “someday” polish

• Showing up faithfully in the one place your people already moved on from

When Visibility Runs Without a Voice

• Posting daily, but every post reads like it could’ve come from anyone in your niche

• A content calendar full, without a cohesive brand anywhere inside it

• Chasing whatever format the algorithm favors this month instead of your own sentence structure and style

• Growing a following that responds to whatever’s trending, and forgets you by the next scroll

• A newsletter that goes out every week and reveals less about you than your out-of-office reply does

Across all dimensions of your business, the pattern repeats: the working half never fills the gap left by the other. It just runs longer, alone—and unremarkably.

That’s the misalignment: a system running on half its parts, pretending it’s whole.

So: Voice + Visibility = Resonance

This is where we stop naming the two failure modes and start looking at what’s actually happening underneath them: why a true note and an open room produce something neither one produces alone.

Zero Times Anything Is Still Zero

Most founders are running their marketing as an addition problem: a little more voice work here, a little more visibility there, and eventually the numbers add up to something that resonates. Right?

Wrong. The problem is that resonance doesn’t add. It multiplies.

A voice that’s completely true, articulated with total clarity, built by someone who’s done every ounce of the values-vocabulary-volume-vibe work you’ll see in the Strategic Insight section below… If it’s multiplied by zero visibility, the end result is still zero.

It changes nothing for anyone but the person working so hard to put their message out into the world.

The reverse holds just as firmly. A visibility engine running at full capacity, showing up everywhere, posting on schedule, algorithm-approved, multiplied by a voice that’s borrowed or vague or missing entirely, is also zero. It’s just a louder version of nothing.

The two half-measures from the last section fail for exactly the same reason. They’re the same math, trying to be solved from opposite ends, landing on the same answer.

The Moment a Signal Genuinely Connects

Think back to the goosebump moment: someone reads your words and thinks, wow, they get me.

That happens when a true note, at the right frequency, reaches a room built to carry it. Voice supplies the note: the specific, particular, unmistakable thing you actually believe, said the way only you would say it. Visibility supplies the room: the platform, the timing, the repetition, the place where that note has somewhere to travel.

The note needs a room. The room needs a note worth carrying.

Resonance lives in the space between them, at the frequency that only shows up when both are doing their job at the same time.

The Ongoing Tuning

Here’s the part that trips founders up most: the tuning never actually finishes.

Founders tend to treat this process as sequential. Find your voice once, lock it into a brand document, move on to visibility work permanently. Build a visibility engine once, then coast on the momentum while the voice underneath stays frozen at whatever it was the day the brand guidelines got written.

The reality is that, as I’ve talked about in many prior E3 issues, this work IS the work.

Why?

Because frequencies drift. Your business changes. Your audience changes. You change. What resonated eighteen months ago can lose its charge today simply because the tuning moved and nobody continued to check the instrument before playing the piece.

Voice and visibility ask for what any real instrument asks for: attention, again and again, for as long as you’re playing it.

🏠 The SPS Philosophy: Voice + Visibility = Resonant Reach

At Sitting Pretty Strategies, the foundational work we continuously recommend has a component to it that is often overlooked even by those founders who recognize its vital importance to their businesses: It’s an ever-evolving practice for the life of the business.

As mentioned above, these parts of your brand’s whole picture need to be revisited as the business—and you—grow, evolve, and shift to better help who you’ve come to serve.

The reason is straightforward. In order to maintain the reach necessary to continue to scale and thrive, the resonance signal must also be maintained at the highest level possible.

The only thing that solves a foundation problem is building—and maintaining—the foundation.

The good news: preserving this equation is sequenceable. There’s an order, and the order matters. Voice clarity comes first. Visibility carries what voice has already made true.

When those two are working together—when the reach and the resonance are aligned—something shifts in how the whole effort feels. The content feels like communication rather than output. The consistency feels sustainable because it’s coming from somewhere real rather than from a content calendar alone. The visibility feels like extending an ongoing conversation rather than performing for an audience you’re not sure is paying attention.

And the right people start finding you. Sharing your work. Forwarding your emails. Mentioning your name in conversations you were never in—because your voice was clear enough and specific enough that they internalized it, and now they’re carrying it.

Reach without resonance is noise. Resonance without reach is silence. You need both. The work is learning how to build them in the right order, and trusting that the order makes the whole thing work better than it could otherwise.

Build the voice first. Then let visibility carry it. When the frequency drifts, because it eventually will, tune again. That’s the actual practice: building, tending, and rebuilding the resonance for as long as the business keeps growing.

🗺️ Strategic Insight: The Four Anchors of Voice

A tuning fork holds one true note, and everything else gets checked against it.

The Four Anchors of Voice framework works the same way: four fixed points that let you check whether what you just wrote actually sounds like you, or like a version of you trying on someone else’s costume.

This anchors one half of the equation. Visibility still needs everything already covered: the platforms, the timing, the repetition, the room. What follows locates the note. Carrying it is the job that comes after you’ve focused the frequency.

The four anchors of your voice are:

Values

Your voice begins with what you actually believe: the principles that survive contact with pressure, on a deadline, mid-launch, even on a bad week. Founders often name values that sound impressive on a slide and abandon them the moment a launch gets tight. The values that count are the ones you keep when abandoning them would be easier.

Vocabulary

This is what you say, and just as importantly, what you refuse to say. Every founder has words that feel unmistakably theirs, and words that feel like a costume. “Honor” instead of “crush it.” “Community” over “clients.” “Invitation” rather than “offer.”

These are evidence of what you actually believe, showing up at the sentence level.

Volume

This is temperature, not decibels: the emotional intensity underneath your words, whether you lean toward conviction or curiosity, whether your natural register runs magnetic and energized or spacious and grounded.

A founder can run at a low, steady temperature and still carry enormous conviction. Another can run hot and animated and still be deeply grounded in curiosity rather than certainty. Neither is more correct. The mismatch happens when your natural temperature and your written temperature diverge, and readers feel the gap even when they don’t have words for the feeling.

Vibe

This is the felt experience on the other end: what it’s like to be the person receiving your words, separate from whatever you were actually saying.

Seen. Soothed. Challenged. Activated. Held. Inspired. Vibe is the emotional residue your voice leaves behind, whether or not the reader remembers a single specific sentence.

Four anchors, one voice. What they add up to, and how to actually put them to work alongside the visibility side of this equation, will be explored in The Understory down below.

🧐 Upon Reflection: Real reach Requires Regular Regulation and Refinement

Visibility without voice doesn’t resonate, but rather it just blends in. And your true voice without the right visibility path is a perfectly tuned note nobody’s in the room to hear.

Your audience isn’t just looking for more content. They’re looking for clarity, energy, and truth. They’re listening for something that feels personal and powerful—a signal in a sea of noise. And that signal starts with your voice.

Going back to the simple metaphor:

Voice is the tuning fork. Visibility is the room it plays in. One sets the note. The other gives it somewhere to travel.

But they must be in alignment. Otherwise? You get nothing but static and distortion, or worse—silence.

Most people think they have a voice problem… Because the content’s out there, but it doesn’t get much traction. Or they think they have a visibility problem because they’re experiencing the amplification of a message that doesn’t actually sound like them.

Both are frustrating conditions.

Really, however, what’s likely happening is outcome from an imbalance equation.

Reach from visibility gets you seen. Resonance from an anchored voice gets you remembered correctly. Businesses that convert are in the right room, at the right time, in front of the right person, with the right message.

And nodding back to the recent issue on AI reading those rooms first, there’s a decent case to be made that this gap is widening, not narrowing, as more of the discovery process moves through AI-mediated search and recommendation rather than a human scrolling and remembering. Visibility increasingly does its real work before someone consciously starts looking—which raises the bar on resonant reach, since what’s being “read” and pattern-matched isn’t a feed impression, it’s the consistency of the signal itself.

Which is exactly why this can’t be a box you check once.

The tuning happens before anyone’s consciously looking, so it has to be happening all the time, in real time, against the actual current register of your brand. Real reach isn’t a moment you hit.

It’s strategic support for the lifeblood of your business..

🤔 Your Turn To Reflect

Take a moment here before you move on. Everything this issue has named about voice and visibility, the frequency, the room, the tuning, the reach, it all points back to the same place: the foundation you’re already standing on. These questions are where you bring it home.

These aren’t audit questions—they’re places to bring honest attention.

Where in your business right now is there visibility with no real voice underneath it, content going out, but nothing distinctly yours inside it?

Where do you have a voice that’s actually true, but you’re the only one who’s heard it yet?

What would it look like to work on both at once this week, instead of treating one as the fix for the other?

One honest answer to any of these is enough to start. Voice and visibility were never a sequence to finish. They’re a practice to keep in tune, together, for as long as you’re doing this work.

Leave a comment

🌀 The Ecosystem Pulse

Before you go—one honest question: Did this issue nourish something?

Reply with a single letter and let me know how this one feels for you:

A — Yes, I feel more aligned

B — I’m still sitting with it

C — This one didn’t connect

Every signal strengthens the ecosystem. Thank you for being part of it.

🧭 Constellation Compass

🌌Elsewhere in the Sitting Pretty Strategies constellation recently:

🌱 In the inaugural issue of The Master Work, I sat with the question of whether the life underneath a business is one the body can actually live inside, and the crossroads of protecting the health of what you’re building versus the health of the person running it. [Read TMW #001 →]

📍In this issue of Pretty Strategic, I made the structural case for coherence as an AI-era indexing requirement—and why the founders who built foundation-first are the only ones a recommendation engine can surface with confidence. [Read PS #039 here ➔]

🌅 In the inaugural issue of The Event Horizon, I explored the phenomenon of when “Human-In-The-Loop” becomes merely ceremony and the oversight designed to keep humans leading and in control of the technology becomes the place where human judgement steadily acquiesces instead. [Read TEH #001→]

🔮 Inbox Alchemy is the lab side of SPS—this is where the ideas across these letters get turned into practice through a sequential, evergreen email experience. [Join the lab →]

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply. Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread.

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

🌿 The Understory

What you just read is the argument. What follows is the architecture.

For Stewards of The🌳Grove who have subscribed to The Understory: what’s waiting for you below is built directly from where this issue has been pointing all along: into the actual tuning work.

This week the work starts with The Frequency Check, a two-part session that takes you through The North Star Voice Statement exercise to anchor the voice half, then a short visibility audit to see where that voice is (and isn’t) actually traveling right now. You’ll walk out with both halves of the equation named, not just the half that’s easier to talk about.

And at the close, I’ll go into a brief, honest note on how I used AI to write this issue, and what that process revealed about this week’s theme. This is a standing section in every Understory issue, because SPS’s position on AI is simple and steady: offload the mundane and repetitive so your irreplaceable creative energy stays where it belongs, in the work only you can do. These tools are built for exactly that.

Let’s get to work, Stewards.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

[Join The Understory →]

Read more

The Master Work is a Sunday letter for founders who have always known: the wider you draw from life, the deeper you can build. The muse of inspiration—and the source of wisdom—comes from the menageries of Life. The songs, the stories, the essays, the experiences, the conversations, the contemplation—all of it feeds the mission. Everything you’re reading, hearing, noticing, and living belongs here, in The Master Work.

Welcome to the first issue of The Master Work—the Sunday letter built on a belief I hold as doctrine: the wider you live, the deeper you build.

This year has not gone according to any plan I’ve laid out. Not by any stretch of the imagination.

As a result of corresponding Family challenges, I have been splitting my time between helping my last living elderly relatives through a hard time while still taking care of my last remaining pet sitting clients. My Aunt & Uncle live in Georgia, and my clients are in Massachusetts. And I have been making the trip back-and-forth every couple of months, driving up and back each time.

All this while working on and in SPS, building this Substack, and balancing other projects that were already in the works when this all began. …It started out as a good idea, splitting my time, attention, and focus between these three big buckets, and I knew why I was doing each one, with conviction.

But we all know what they say about that damn road to hell…

I love my Family dearly, and though for most of my life I have felt like an alien in my own world, taking care of those you love has always been a core pillar of what I understand—and want—Family to be. And I have kept a handful of my favorite pet sitting clients on because leaving cold turkey after ten years was harder than I thought it would be. They are my Family, too.

Plus, is there anything better than fun time with your best furry friends??

Add to that mix that I have an innate, almost compulsive, need to help whenever and wherever I can, I find myself here… At the crossroads of my helping and my health.

Have you ever done that?

Essentially sacrificed your self, your health, at the altar of trying to help someone else with theirs?

That is where I find myself now.

🌱 The Seed

So what does this dilemma I’m experiencing have to do with you?

In this first issue of The Master Work, I want to explore a hard question we as entrepreneurs often choose to just push past: when the mission and the body compete for the same resources, which one actually gets fed first?

Conscious founders are exceptionally good at protecting the health of what we’re building and who we serve. Protecting the health of the person running the whole operation is often the one job that silently falls to the bottom of the list—or worse, falls off it completely.

Does this reveal an inkling close to home?

Sometimes that life we’re working so hard to build is made of things that can drive us—or drive us nuts, sometimes it includes things that can scare us—or simply stress us out, and sometimes, if we’re lucky, it includes the path to our destiny and our legacy. I believe every business gets stronger, however, the moment a founder stops treating their recreation, their rest, their curiosities, and their bodies as separate from the work.

We are whole beings having a whole experience in this experiment called life. And in every decision we make, every step we take, and every ecosystem we create, there’s a thread that can be followed across the menagerie of our life.

This week’s thread found me at the intersection of my own reading, rest (or rather lack of it), curiosities, and awareness of my own body. I’ve been stretched pretty thin, and feeling fairly stressed out on the regular with the current situation. A question that has been traveling with me for a while keeps coming to the surface in some way, shape or form recently: am I building a life my body and nervous system can actually live inside?

I went looking for what that question was really asking of me, and kept circling back to that one line above from Emerson, written more than a century and a half ago: the first wealth is health.

Conscious founders can know this instinctively, even deploy rituals into their lives to begin to honor its wisdom, and still equally choose to ignore it in pursuit of all things professional and progress-oriented. I think we can all agree that is its own kind of contradiction, and worth sitting with for a bit.

So that’s where we’re starting. The actual operating system underneath everything you’re trying to build.

Subscribe now

📖 The Work

As a result of the situation I described above, I’ve been thinking a lot this week about how we as conscious founders often give so much of ourselves in service of the health of our businesses, and in service of others, that we forget to keep up those rituals and routines that keep our own health at optimal levels.

We forget that, while the passionate and persistent pursuit of our mission will bring us the kind of wealth we all long for—freedom, independence, influence—the first wealth is always health. Without it, no other kind of wealth carries the same weight.

Without health, the canopy may look full, but the roots aren’t strong, and the fruits don’t bloom the same way because they are planted in depleted soil.

And no matter how hard we push through, suck it up, or press on, the ironic reality remains: a business can only grow as well as the nervous system of the person running it.

The nervous system is the body’s communication network, sending messages between the brain, spinal cord, and the rest of the body. It carries signals that tell the muscles when to move, the organs when to work, and the brain what the body is sensing.

You can think of the nervous system as the understory, the forest beneath the forest: an underground network of signals carrying messages through the body, helping every part know when to move, rest, protect, or respond.

Similarly, a business ecosystem is your brand’s nervous system, because it senses, coordinates, and responds to the signals within it.

In the body, the nervous system carries signals that help different parts of the body work together. By the same token, in a business, people, teams, data, and systems do the same thing by moving information and triggering action. So, the nervous system is to the body, what information flow is to a business ecosystem.

When communication is strong, the business behaves like a healthy nervous system—fast, coordinated, and adaptable. Conversely, a broken information flow is like damaged nerves signaling the body: everything slows down or misfires, and things begin falling through the cracks. It starts small, until, eventually, the entire system is malfunctioning, and your business effectively comes to a grinding halt.

In the most literal sense, this is what happens to your body (and it begins with your own nervous system) when you start neglecting your health.

It starts small, and this is often why we don’t react to the signals right away. Maybe you notice a little weight gain, and your eyesight feels a little off when reading. You’re thirsty more of the time, and when you get up out of your chair, it takes a bit more effort than you remember needing before. You try like hell to remember a word you know should just be coming to your tongue, and you also notice you’re a bit more irritable and aggravated more of the time than you used to be. Your brain feels foggy, and you feel tired more often, regardless of how much you sleep.

Each of these things individually is a signal of something going on in the body. It can be a one time event, or it can be a symptom of something else evolving. Collectively, though, while seeming unrelated, they all add up to the body reacting to overwhelm—and its not-so-distant cousin, burnout.

And with the breakneck speed of life and business these days, it’s become more than a luxury to earnestly focus on our health and well-being; it’s a necessary and essential practice. And it begins with calming our nervous system and quieting ourselves enough to hear the signals it sends. Only then can we make the rituals and routines we employ to maintain our health serve what our body actually needs from us to do so.

For conscious solo founders, health is not separate from the business—it is the foundation that makes the business possible. Just as a nervous system keeps the body aware, regulated, and responsive, your health determines how clearly you think, how steadily you execute, and how well you recover from stress. You can build a strong offer, a smart strategy, and a growing audience, but if your body is depleted and your mind is overextended, the business will eventually feel that strain.

That is why health is the first wealth: it is the inner infrastructure that supports every decision, every relationship, and every season of growth.

😊 Happiness Habit of the Week

And infrastructure like that doesn't run on motivation—it runs on maintenance. So here's the one ritual I keep coming back to, the practice that's done more for my own nervous system than anything else I've been able to implement.

Solo founders do not need perfect wellness routines; they need repeatable practices that bring the body back to safety, clarity, and rhythm.

The key is to build habits that continually help protect your health, and in particular your nervous system.

Here’s one practice that can make a world of difference in the way your day unfolds, as well as your cumulative days. Admittedly, if you’re anything like me, it might take a bit of effort up front to set the habit, as you’ll be used to the go-go-go get it done mentality.

The practice has been written about in multiple ways, and I’ve personally heard Brendon Burchard talk about how he incorporates a version of it into his days.

The idea is simple: for every hour of work, take 5-10 minutes to reset and recenter. Step away from your computer, move your body, hydrate, and breathe.

Simple, as we all know however, is not the same thing as easy.

When I first began implementing this practice into my day, I had to set alarms (multiple), because I would look at the clock, do some kind of ridiculous math in my head, and then get back to work saying I’d take a break in a few minutes. Yeah, right! …Two hours later I’d be dying of thirst, my legs would be cramping, and my eyes would be scratchy and tired. And let’s face the facts—I wasn’t doing my best work anyway, because my body was in turmoil and I had been ignoring it in the name of “just one last thing…”

So, if you get caught up in the “one last thing…” cycle like I do, do yourself a big favor, and let that microcomputer in your pocket do one of its other jobs and remind you when it’s time to move and drink and breathe and rest.

The nervous system is what helps the body stay in relationship with itself. Health does the same thing for a founder’s life and work: it keeps energy, judgment, and resilience in conversation with ambition. Help it do its best work, and it will always help you do yours.

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🎨 The Mosaic

We’ve arrived at the living proof of both the issue’s premise, and the edition’s: A life lived wider allows the human at the center of it to build deeper.

So the mosaic is a collection of sources relating to the issue’s topic, that you can add to your menagerie.

This week’s mosaic found me while I was mid-crisis, or mid-drive, or just mid-everything else so far this year. What I noticed once I allowed myself to sit still long enough to look: a piano ballad from 1977, a poem I found when researching ecology last year and return to for comfort, an old museum exhibition page I stumbled into while exploring this topic, an hour of someone else’s voice on rest, and a headline about founders finally doing something different.

All five say the same thing in a different language: your body was never separate from the mission. Here’s why each one earned its place in this first issue.

The Five Sources:

🎵 “Vienna”—Billy Joel

“Slow down, you crazy child, you’re so ambitious for a juvenile.” Joel wrote it to himself in his twenties, and it’s aged into something closer to a warning label than a lullaby. It’s the most direct musical statement of this whole issue’s tension: ambition doesn’t wear out a person. An unbroken pace does.

📜 “Wild Geese”—Mary Oliver

“You only have to let the soft animal of your body / love what it loves.” The most direct permission I know of in poetry: permission to stop earning your place through self-punishment, and to trust that your body’s own wanting is trustworthy enough to guide the mission.

🖼️ “A Picture of Health: Art and the Mechanisms of Healing”—Allen Memorial Art Museum, Oberlin College

I found this while researching this issue: a 2016 exhibition built around one idea—“art has been perceived to bring about the health and well-being of its makers and beholders.” The exhibition is a decade old. The idea isn’t. It’s the same argument this whole issue is making, just aimed at paintings instead of businesses: healing happens in the things we make, the same as it happens in the bodies that make them.

🎙️ Tricia Hersey—”On Rest as Resistance,” For the Wild podcast (episode 185)

Hersey’s fuller conversation on the history and stakes of grind culture, and rest as a slow, meticulous love practice. Rest, in her telling, is part of what makes the building possible at all.

📰 “The Founder Burnout Crisis: Why More Entrepreneurs Are Redesigning Their Work Lives in 2026”—The Successful Founder

Coverage of founders actively rebuilding their businesses and schedules around what their bodies can actually sustain. After four sources that name the problem, this one is the counterpoint: people are already doing this, right now, and building real businesses while they do it.

🌿 What’s Taking Root

This week, I’m sitting with the same question that opened this issue:

Whether the life underneath this business is one my body can actually stay healthy in?

That clearer question already feels like real progress—the kind of place where the actual building starts.

🌀 The Ecosystem Pulse

Before you go—one honest question: Did this issue nourish something?

Reply with a single letter and let me know how this one feels for you:

A—Yes, I feel more aligned
B—I’m still sitting with it
C—This one didn’t connect

Every signal strengthens the ecosystem. Thank you for being part of it.

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply. Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread.

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

🧭 Constellation Compass

🌌Elsewhere in the Sitting Pretty Strategies constellation recently:

🗺️In this issue of Elegant Email Ecosystems, I dove deep into what AI surfaces about your business before any human decides to trust you, the four signals it scans for, and how to build for the evaluation you didn’t know was happening. [Read E3 #027 →]

📍In this issue of Pretty Strategic, I made the structural case for coherence as an AI-era indexing requirement—and why the founders who built foundation-first are the only ones a recommendation engine can surface with confidence. [Read PS #039 here ➔]

🌅 In the inaugural issue of The Event Horizon, I explored the phenomenon of when “Human-In-The-Loop” becomes merely ceremony and the oversight designed to keep humans leading and in control of the technology becomes the place where human judgement steadily acquiesces instead. [Read TEH #001→]

🔮 Inbox Alchemy is the lab side of SPS—this is where the ideas across these letters get turned into practice through a sequential, evergreen email experience. [Join the lab →]

📬 Write Back

Where did this week’s thread show up in your body this week?

Join the conversation and tell me what you noticed. One honest line is enough. I read every reply.

✨ Here’s to building something worth the body that has to carry it.

To everything that feeds you,
~StacyLynn
Founder, Sitting Pretty Strategies
Root Deeply. 🌳 Rise Differently.

P.S.

And if you know a founder who’s been running on fumes and calling it commitment—send this their way. Sometimes the right thread finds you through someone else’s hands. 💌

The conversation about AI has gotten very loud. So far, in that conversation aspects about the human experience in the AI era have gotten a bit lost.

The Event Horizon is a Thursday letter built for founders who want to look clearly at what AI is actually doing—to our work, our thinking, our identity, and our businesses—and lead from that clarity rather than react from the noise.

We’re not here to chase the tools or debate the hype. We’re here to figure out what it means to stay human while building something powerful—and to do that work together, week by week.

Human lead. AI empowered. Always.

Welcome to the inaugural issue of The Event Horizon! 🎉

I started this Rooted & Rising edition because the conversations around AI have gotten very loud, and most of it is noise and not signal to be zeroing in on to stay in the lead in this era. …Tools, benchmarks, capability races, workflow hacks—the noise is real. The Event Horizon aims to raise the level of conversation, and address a different part not being had enough: what all of this actually means for the human underneath the productivity gains.

I want to ask different questions to bring better, more consequential, conversations to Stewards in The🌳Grove: What does it mean for our thinking, our judgment, our sense of agency, our identity, our capacity to stay in the lead?

This first week, we’re starting with addressing the safeguard that was supposed to keep humans in control of the technology—and what’s becoming of it.

📡 The Signal

“Human-In-The-Loop” (HITL) began as a design idea. It was the mechanism in interactive computing and human-computer interactions that selectively included human participation rather than fully removing humans from the process and reverting to automated systems by default.


It’s kind of like autocorrect on your phone.

Your phone is perfectly capable of making suggestions. Sometimes it fixes a typo before you even notice it. It’s lovely and helpful, with tiny digital butler energy.

But other times, your phone is very confident. Aggressively so, even. It will suggest the word it thinks you meant, swap out your spelling, and occasionally turn a normal sentence into something that makes you question the entire future of technology.

That’s where the human comes back in; you’re still in the loop.

You don’t need to personally type every letter with a quill and candlelight. But you do need to glance at the suggestion and decide whether the machine helped… Or was wildly wrong, and only one unfortunate tap away from sending your mother a message you’ll never emotionally recover from.

That’s the basic idea of human-in-the-loop.

The system can assist. It can predict. It can speed things up.

But the human still stays involved where judgment, context, and “please don’t send that” matter.

There’s long been an idea in technology that humans should supervise, correct, and guide automation in situations where judgement and discretion are a vital part of the process.

Autocorrect is one little loop.

The system suggests a word. You accept it, reject it, or mutter something unprintable and fix it yourself. Generally, this is the camp I end up in.

But imagine if accepting that one autocorrect didn’t just change the sentence.

Imagine it also updated your calendar, sent a follow-up email, made a list of your groceries, changed the status of an important project, and notified three people in Slack on your current progress.

Now the human-in-the-loop role is no longer “check the suggestion before it goes out.”

It becomes “understand the cascade before it starts.”

And that is where the modern HITL conversation gets much more complicated.

As agentic AI becomes pervasive in organizational processes, its development has made HITL a volume operation for humans. The paradigm is no longer 1:1—one AI output, one human evaluation. In reality, it’s not one task being automated. It’s one action triggering another, then another, across the system.

It’s a domino effect, but with AI making the moves.

And in that environment, HITL becomes something different from what it was originally designed to be.

In researching the topic this week, I came across State of AI Trust in 2026: Shifting to the Agentic Era, a report on the findings of the McKinsey 2026 AI Trust Maturity Survey [see the original source here ->]. One line stood out, summarizing the HITL dilemma at hand:

“If a human receives 200 requests a day, they will stop looking at the evidence.”

The human in the loop was supposed to be the safeguard. The research is beginning to reveal it to be the very place the loop is most likely to collapse.

🪨 First Principles

Staying in the loop is not the same as leading it

The Sitting Pretty Strategies (SPS)’s position—AI for the mundane and repetitive, human energy for the creative, strategic, evaluative, irreplaceable—was always implicit in what HITL was supposed to be. AI handles the work; human stays in the judgment role. That’s the design.

What this week’s signal reveals is that when oversight becomes the job—and it’s 200 approvals a day—the oversight itself becomes the mundane and repetitive. Human energy ends up in exactly the place SPS believes it shouldn’t: processing volume, not exercising judgment.

This gives way to the idea that there are two versions of “staying in the loop.“

Genuine discernment—evaluating, questioning, intervening. And procedural compliance—approving, moving on, approving again. The first protects the human operating system. The second doesn’t just fail to protect it—it depletes it.

For founders, this means something specific.

While large organizations lose institutional judgment capacity when this happens, something more damaging can happen for the solo founder. For a solopreneur or small team, this kind of oversight means losing something harder to rebuild—the discernment and reasoning that distinguishes their work.

There’s no institutional backup. No extra layer overlapping individual loss of judgement capacity. For the small business founder, the operator and the business are one.

What atrophies in the person disappears from the business.

My belief, as a solo founder, is not only “offload the mundane, keep the irreplaceable.“ It’s also “protect the capacity for the irreplaceable by exercising it—even when the tool could do it faster.“

And I am on this journey with you.

Discovering the amazing things AI can do for my business. Then discovering the consequences of allowing it to do everything it has capacity to do. And arriving now at the part of the journey that was always the work: keeping what’s inherently and creatively me strong and clear—my cognitive sovereignty, discernment, and independent thought—and delegating only what helps me support and maintain my energy in order to be able to scale my impact.

Which brings us inside the loop itself—to what’s currently happening to a founder’s judgment, reasoning, and sense of authorship when oversight becomes ceremony.

🫀 The Human Layer

Are you forming thought, or receiving it?

This isn’t the first time the conversation on cognitive sovereignty has been an important discussion. Throughout the history of technological advancement, there has always been a component of concern on the cognitive sovereignty layer of being human.

Cognitive sovereignty is the idea that people should retain control over their own thinking—their attention, judgement, memory, and decisions—and avoid environments that would steal and steer it manipulatively or coercively. Staying the author of your own reasoning.

There’s a gap between feeling like you’ve reasoned something through and actually being its author—and with the rise of AI, most of us have stopped noticing the difference.

The biggest concern in this area is that AI, recommendation systems, and data-driven platforms can shape what we notice, believe, and choose—before we realize it, or even without us being aware at all.


It’s not just in getting a bad or wrong answer, and not challenging it.

The worse scenario is the loss of the habit of even checking and verifying the output—or questioning and reasoning it out for ourselves, no matter the quality of the output.

It’s very much a “use it or lose it” kind of capacity.

Cognitive sovereignty asks, “Am I still genuinely forming this thought, or is something else doing too much of the thinking for me?“ And I think we all should be asking that question more often, as well.

And I am, more and more. I’ve also begun taking back certain aspects I originally handed over to AI joyfully as I learned what it could do. My goal is to have a better balance as I move forward.

You see, I have simultaneously a hopeful and realistic view of what AI can do for us, as well as to us, in the coming years. And at the same time, I also hold a dichotomy of views on human potential and what it means to be human.

I don’t think that just because you get your information from an AI source that inherently means you have given up your ability to think for yourself. In fact, I would argue that most thought is built by other thought.

All research is, by its very nature, outsourced to external sources. All thinking is shaped by context and the world around us. This is where the crucial skills of interpretation, reflection, discernment, and critical thinking come into play.

So, I don’t think this is about becoming isolated from influence, AI or otherwise. What makes something our own thought is the reflection and the refinement and the time we take to actively think through the information we have taken in, and to then form our own output insight about it.

I also don’t think the answer to maintaining our cognitive sovereignty is to stop using AI. Using the tools is like using any other tools that have changed the landscape before. It starts with a strong mind, and strong character, and a strong intention of boundaries.

And those boundaries begin with critical thinking.

It becomes—history has shown time and again—one of the main skills critical for humans to not only develop but also ritualize and maintain. It is one of the main tools that makes cognitive sovereignty possible. Critical thinking helps you evaluate claims or spot gaps and test assumptions for yourself, while cognitive sovereignty is the broader condition of still being able and in charge of your own attention, your judgments, and your authorship of thought while you do your critical thinking.

Critical thinking is the active skill of questioning and comparing ideas. It’s a practice. It asks, “Is this true?“

Cognitive sovereignty is the right and capacity to keep that questioning genuinely your own. It’s the state of not surrendering the practice to automation or persuasion or habit. It asks, “Am I still the one deciding how to think about this?“

In this article on critical thinking in the age of AI [see the source material here ->], author Sabryna Alsfasser reasoned that valuing speed over cognition and judgment is the root condition of decline in the HITL process. And this moment has elevated critical thinking from a soft skill into something closer to the core infrastructure of the human operating system.

I don’t think this all should overwhelm or dismay us.

Rather, the smart play for conscious founders is to earnestly categorize the autonomy (what you keep as human as possible) and the automation (what you delegate through a human lens). The founder who names this, who protects the internal synthesis layer, who practices deliberate discernment even when the tool could move faster—you’re not being anti-AI.

You’re doing exactly what the AI era actually requires: Operating from a stance of human lead, and AI empowered.

🌅 Light on the Horizon

When humans stay the author of the impulse

The Creativity Paradox

Researchers studying AI’s effects on creative output found something worth pausing on: AI enhanced human creativity—but only when the human initiated the idea first.

When participants let AI generate the opening concept and worked from there, their output was measurably less original than when they brought their own seed idea and used AI to develop it further.

The tool did more when the human started. It did less when the human deferred.

For founders, this means the initiation layer isn’t the part to hand over—even when the tool can handle it. The creativity lives in the start. The augmentation lives in what follows. Human-generated → AI-developed → human-refined is a different cognitive loop than AI-generated → human-approved. The first keeps authorship intact. The second is the ceremony problem in miniature.
[Read the research →]

The Diagnostic Horizon

On a different front: AI is compressing the rare disease diagnostic odyssey in a way that shows exactly what human-led, AI-amplified looks like when it’s working as intended.

Families living with rare diseases often wait seven years or more for an accurate diagnosis. AI systems parsing genetic markers, symptom clusters, and research literature are shortening that timeline dramatically—surfacing pattern connections that human specialists, regardless of expertise, couldn’t hold in working memory simultaneously.

Physicians remain the diagnostic authority. AI extends the reach of that authority.

Nobody removed the human from the loop. They gave the human a tool that made the loop worth being in.

That’s the version we’re building toward.
[Read more →]

⚠️ Concern Worth Naming

Something worth watching:

You may feel like you’ve reasoned something through independently—and still not have been the author of the conclusion. This is the subtler risk. When AI wraps its output in language that sounds finished, the illusion of independent thought is easy to miss.

What looks like clarity can be compression. What looks like insight can simply be plausibility. The reasoning feels like yours because it reads like yours.

The good version of this looks like:

A founder who pauses before accepting any AI-synthesized conclusion and asks: Did I think this through, or did I receive it? The habit of that question—practiced regularly, even when the output looks right—is what keeps cognitive sovereignty from becoming a concept you agree with but never embody.

That questioning is the loop. Stay in it.

📡 Signal Back

This week’s question:

Where are you in this? Are there places in your work where you’ve noticed your oversight becoming a formality—where you’ve stopped genuinely looking, even when you’re technically still in the loop?

Reply and help me see the whole picture.
One honest line is enough. I read every reply.

🎯 Your Move

Where in your work have you assigned yourself to “oversee” something you’ve actually stopped genuinely looking at?

Volume has a way of making deep looking feel impossible. Name one place where your human judgment has become a procedural signature.

Then: this week, review that one thing as if it were the first time.

Ask a question about it. Disagree with it, even briefly. Play devil’s advocate, or the steelman. …The act of genuinely evaluating is the practice—and the practice is what keeps the capacity alive.

🌀 The Ecosystem Pulse

Before you go—one honest question: Did this issue nourish something?

Reply with a single letter and let me know how this one feels for you:

A—Yes, I feel more aligned
B—I’m still sitting with it
C—This one didn’t connect

Every signal strengthens the ecosystem. Thank you for being part of it.

🧭 Constellation Compass

🌌 Elsewhere in the SPS ecosystem this week:

🗺️ In this issue of Elegant Email Ecosystems, I dove deep into what AI surfaces about your business before any human decides to trust you, the four signals it scans for, and how to build for the evaluation you didn’t know was happening. [Read E3 #027 →]

📍 In this issue of Pretty Strategic, I made the structural case for coherence as an AI-era indexing requirement—and why the founders who built foundation-first are the only ones a recommendation engine can surface with confidence. [Read PS #039 →]

🔮 Inbox Alchemy is the lab side of SPS—where ideas like these get turned into practice through a sequential, evergreen email experience—the hands-on companion to everything SPS publishes, one issue at a time. If you’re ready to move from insight to implementation, [Join us in the lab →]

If this issue resonated with you, I have four small asks:

① Hit the ❤️—It takes one second and tells Substack this conversation is worth having.

② Hit the 🔄 restack—It puts this in front of your followers—the ones who are already building differently and don’t yet know there’s a name for what they’re doing.

③ Share this issue with a founder you know who could benefit, and might even think you’re their hero. 🦸

Share

④ Drop a comment—I read every one. And I reply. Some of my best thinking happens in response to what you bring to the conversation here—and future issues often start in a comment thread.

💡The right idea finds the right person at the right time. You might be the one who gets it there.

Leave a comment

🌿 The Understory

What you just read is the argument. What follows is the work.

What comes next was built for The Understory—and it goes where this issue has been pointing all along: into the practice of staying genuinely in the loop.

This week’s Understory is two things. A Practice Protocol—one concrete, daily practice for founders who want to keep their judgment engaged rather than let it drift into a procedural signature. And a Research Deep Dive: the fully annotated source set behind this issue’s argument, including the Wharton “cognitive surrender” study, the HBR Brain Fry findings, and three additional Tier 1 and Tier 2 sources—built for the educators, coaches, and expert-to-educators who’ll carry these ideas into their own work.

And at the close—a brief, honest note on how I used AI to write this issue. This is a standing section in every The Event Horizon Understory, because my position on AI is simple and steady: use the tools, stay in the loop—stay in the lead. These are not in conflict. The Understory is built on exactly that premise.

Let’s get to work.

For everyone reading from The Canopy: this is what The Understory looks like. Join us in the deeper work.

Join The Understory →]

Read more

Welcome back to Elegant Email Ecosystems, where we believe the work of building a coherent foundation was always about more than connection—and this week, we’re meeting the interpreter you didn’t know you had.

In this issue, we are going to bring you a revelation on AI (I know, I know, but hear me out…), that you might not even be aware affects you yet.

The conversation most founders are having about AI splits cleanly into two fronts.
① How do I use it better?
② How do I compete with what it’s replacing?

Both are real questions. And both deserve attention.

But there’s another equally important question buried beneath them—one most conscious founders haven’t thought to ask yet:

Who is reading my business’s online signals right now, before any human ever decides to trust me?

The answer is AI.
And the evaluation has already begun.

Somewhere between your last blog post and your next sales call, something already happened without you even knowing. An AI system read your website, parsed your LinkedIn posts, cross-referenced your “about” page against your service descriptions, and then quietly formed a judgment about whether you are who you say you are.

No human was in the room. No human needed to be.

This is one part of the AI conversation that conscious founders aren’t actively involved in yet, because most of us are still stuck on the productivity question: can it write my emails, can it summarize my meetings, can it save me four hours a week?

And again, those are fine questions, but they’re just the wrong size for what’s actually happening to the landscape of business.

The Evaluation That Happens Before Discovery

Let’s talk about what shifted while most people were focused elsewhere:
websites used to be destinations. Content was the brochure. And your profiles were the flyers posted to a community bulletin board at the coffee shop or library.

And it worked, in the old world.

In the old model: You built something, drove traffic to it, a human typed keywords, scanned a page of links, clicked several sites, and made a choice. The human was the first reader, though, whatever decision they made.

The reality of the customer journey now is that it’s shifting, and that shift matters more than you think, and coming quicker than you probably realize.

The new model is increasingly a different scenario: a human asks an AI for an answer, shortlist, or recommendation. The AI synthesizes information from multiple sources, and, in a growing number of cases, an agent can continue into comparison, booking, checkout, or post-purchase support.

All without the human ever coming across your business or brand directly.

The AI systems shaping your business’s experience right now aren’t only the tools you choose to partner with. They’re also the unseen evaluators sitting between you and every person who might one day become your client, student, or subscriber—the recommendation engines, the answer engines, the assistants your future customers are asking, “Who should I hire for this problem I need to solve?“

And before any of those systems will vouch for you, they’re doing something founders have never had to account for before: they’re reading you and your business for coherence.

The external architecture of the customer journey is changing. When someone asks ChatGPT, Claude, or Perplexity—or even Google—to recommend an email strategist or a business coach or a content consultant, the AI doesn’t return a list of links. It distills. It reads your website, your content, your professional presence, and it decides whether you’re worth recommending.

And in this model, often a human never sees your name.

In this version of the world, websites, content, and profiles become less about the first-impression experience and start being more about the evidence they give.

For solo and small expert-led businesses, the key shift is not “SEO is dead.“

It is that discoverability is being re-layered. Traditional rankings still matter because AI systems still need crawlable pages, eligible snippets, and structured signals. But ranking alone is no longer enough. These systems increasingly decide which claims to quote, which entities to trust, which offers to compare, and which actions they can safely complete. That significantly raises the value of brand clarity and first-party expertise.

The part worth sitting with: the AI is reading for coherence.

Voice. Values. Specificity. Consistency. These are the signals AI systems—both the tools you use internally and the platforms that might surface your business externally—are scanning for before the first human hand-off ever happens.

A business with clear, consistent voice across its content and platforms reads as authoritative. A business whose LinkedIn bio contradicts its website, whose emails sound like a different person than its newsletter, whose offer language shifts with every new trend—that business reads as fragmented.

Fragmented doesn’t get recommended. It gets passed over, without a single human ever knowing it was even in consideration.

Most founders are building for the human at the end of the journey. It’s been the smartest play for years… Build for the one who clicks the link, reads the email, books the call. That human matters enormously—still. The problem is that, increasingly, they are no longer the first reader of your business.

The AI is.

And the AI is reading something very specific: your foundation. The clarity of your expertise. The consistency of your voice. The specificity of your point of view. The coherence between what you say you do and what your content actually demonstrates you understand.

The more aligned those signals are, the more readable you are to AI. The more readable you are to AI, the more likely you are to be surfaced when the right person asks the right question.

🎙️ Real Founder Confessions

I have been down an AI rabbit hole for the last 15 months, and am pretty heavy into actively building with AI this year.

As such I spend a lot of time now in rooms where that is the primary conversation. And one of the main threads of that conversation, that a very small percentage of people are discussing, let alone building for, is how AI has quietly taken over search, and sooner than we all think, will engender the entire experience of being found as a business or brand in the online marketplace.

Now, even if you haven’t used AI to search—or buy—anything online yet (and if you use google, like 90% of the rest of the online searching world, you have), you can begin to see why having this conversation now is so important.

Think about the last time you prepared for something important in your business.

Maybe it was a sales call.

You pulled the prospect’s LinkedIn before the meeting, skimmed their recent content, thought through what they might be worried about, what questions they’d probably ask. You were doing the invisible work of meeting someone where they already were—before they knew you were paying attention.

You arrived informed. You arrived ready.

Now consider this: something is doing the exact same thing to your business. Right now. Before you schedule the call, before the prospect sees your name in their inbox, before any human has been given the chance to google you, click through to your website, or read a single thing you’ve written.

That something is AI.

The evaluation it runs is invisible, continuous, and—for most business owners—completely unknown.

No one told them it was happening. Most still don’t know.

By reading this, you now know. That changes something—even if you haven’t decided what to do with it yet.

The Gap Between Your Intention and Your Signal

Every successful business, from the boutique coaching practice to the scaling digital education brand, has one thing in common: coherence. And when it’s lacking–or missing altogether, it detriments your business on every level—including the AI-mediated discovery process.

But what are the symptoms of the gap between your intention and that signal that gets you recognized, recommended, and remembered? Let’s look at some across the most important aspects: voice, values, specificity, and consistency,

VOICE

What voice drift across platforms looks like:
Your LinkedIn sounds like one person, your newsletter sounds like another, your about page sounds like a third.

You know they’re all you—AI is building an identity from the pattern, however, and the pattern doesn’t resolve into a single coherent signal.

There’s AI in that there voice:
You’ve been using AI to draft more content, and the voice has slowly normalized toward AI defaults—structured, balanced, slightly formal. There’s no you there.

What AI reads is a business that sounds like AI. Which means it’s not reading a distinctive voice at all. It’s reading a mirror.

VALUES

When your values are stated, but not demonstrated:
Your about page says “human-first, integrity-led.” Your content is 90% tactics and frameworks with little relational underpinning.

AI reads content patterns, not bio claims. The claim and the demonstration have to match.

SPECIFICITY

You have vague transformation language:
“Helping coaches grow their business” dissolves into the noise. “Turning expertise into an automated, evergreen teaching and income engine” is specific enough to be found, cited, and recommended.

The former is everywhere. The latter is yours.

CONSISTENCY

The indexed-vs.-current gap:
This one is specific to AI: your oldest, most-linked content may still be positioning you as something you’ve moved beyond. AI reads age and inbound links as authority signals.

If your 2022 content outweighs your 2025 positioning in the index, the old signal is still the loudest one in the room. Make sure it’s congruent with the signal you’re trying to send.

What “Coherence” Means to a Machine

Coherence isn’t a vibe.

It took me longer than I’d like to admit to understand this. I thought coherence was something you felt your way into—an emergent quality that arrived when all the work was good enough. What I eventually learned is that it’s more like load-bearing architecture. You can feel it when it’s working. But it holds because of structure, not because of intention.

And to a language model, that’s precisely what it is: structural. It’s the degree to which everything you’ve put into the world—your voice, your stated values, your specific language, your consistency across platforms and time—actually holds together as one recognizable thing.

Humans forgive incoherence constantly.

We meet someone at a conference, like their energy, and decide to overlook the fact that their website still says “coming soon” or that their LinkedIn voice sounds like a different person wrote it. We fill in gaps with goodwill, because we’re pattern-matching against a whole person standing in front of us.

AI systems don’t have goodwill.

They have data, and they have pattern recognition trained to notice exactly the kind of fragmentation a generous human would smooth over. A values statement that says “people over profit” sitting three clicks from a pricing page that reads like a hard upsell. A founder bio describing deep expertise next to content that’s generic enough to have been written by, well, an AI with no particular point of view. A brand voice that’s warm and specific in the newsletter and flat and corporate in the case studies.

To you, those might feel like minor inconsistencies—different content, different day, different mood.

To a system trained to detect pattern and anomaly, they read as signal. Either this business doesn’t know what it is, or it’s performing something it hasn’t actually built. Both conclusions are quietly disqualifying.

Coherence as Infrastructure

Why Specificity Is the New Trust Signal

There’s a reason vague positioning used to work and increasingly doesn’t.

A human reading “we help businesses grow” might assume good intent and ask a follow-up question. An AI system reading the same phrase has no follow-up question to ask—it has only what’s been published, and vague language gives it nothing to anchor an interpretation to.

Generic language doesn’t read as confident to a machine. It reads as unverifiable, and unverifiable doesn’t get recommended with conviction.

Specificity, by contrast, is legible. “We help service-based founders rebuild their email systems around a five-part architecture we call the Pentagon” gives an AI system something to hold onto, repeat accurately, and connect to a real human need. It’s also, not coincidentally, exactly the kind of language that builds trust with an actual human reader.

The interesting thing about building for AI legibility is that it rarely diverges from building for human trust—it just removes your ability to hide behind ambiguity in either direction.

This is where values come back into the equation, not as a marketing layer but as load-bearing structure. A business whose stated values show up consistently in its pricing, its policies, its tone, and its actual client experience is coherent in a way that’s detectable. A business whose values page exists separately from how it actually operates creates exactly the kind of contradiction these systems are built to notice.

The founders who will be well-served by this shift aren’t the ones who write the best values statement. They’re the ones whose values were never separate from their operations to begin with.

From Discovery Layer to Decision Layer

It’s tempting to file all of this under “SEO, but newer”—another technical layer to optimize once the real work of building the business is done.

That genuinely undersells what’s actually shifting.

Search engines, at their peak, were a discovery layer. They helped a person who already knew roughly what they wanted find a list of options they then could evaluate themselves. The evaluating was still entirely human—the comparing, the gut-checking, the deciding who felt right.

AI systems are moving into something closer to a decision layer.

When someone asks an AI assistant to compare three options, or to recommend the right fit for their specific situation, the system isn’t just surfacing a list—it’s doing an early pass of the evaluation itself, and effectively narrowing the field before a human ever opens a tab.

That early pass is built entirely from coherence signals: which businesses describe themselves with enough consistency and specificity to be summarized accurately, and which ones generate a flat or uncertain characterization because their own materials don’t agree with each other.

This means the customer journey itself is being compressed and reordered.

The old model assumed a human moving through awareness, consideration, and decision while encountering your brand fresh at each stage and forming an impression as they went.

Increasingly, however, a meaningful part of that journey now happens before the human arrives—inside a system that has already formed a working interpretation of you, and is already shaping what the human sees, how it’s framed, and whether you’re in the running at all. By the time a real person lands on your site, they may already be carrying a borrowed impression of you, assembled by something that read your foundation before they did.

For founders, that’s a reason to take the foundational work more seriously than ever—because the foundation is no longer just for the humans who eventually find you. It’s also, more and more, for the interpreter standing in front of them.

🏠 SPS Philosophy: Coherence Is the Load-Bearing Wall

At Sitting Pretty Strategies, we’ve always built foundation-first. The voice work before the content calendar. The clarity of expertise before the marketing push. The coherence of values across every surface before the offer is ever written.

We don’t build that way because it was the fastest path to revenue. We build that way because we have always understood something about the relationship between trust and legibility: the business that sounds like itself—clearly, consistently, across every place it shows up—is the business that earns the kind of trust that compounds.

The reality to what this moment in AI is doing, for the founders paying attention, is simply making that argument impossible to ignore now.

AI systems are reading your foundation the same way a discerning human would—looking for coherence between what you say you stand for and what your content actually demonstrates, between the specificity of your expertise and the vagueness of your positioning, between the voice you claim and the one that’s actually showing up across your platforms.

The difference is that a human might give you grace and overlook the gaps.

They might feel the warmth underneath the inconsistency, give you the benefit of the doubt, and stay anyway. AI has no warmth to feel. It has only what’s been published, and it is looking for one thing: a business that gives the same signal across all the places it can be discovered.

This is not new work.

This is the work SPS has always named as foundational—and the reason we’ve always insisted on doing it before anything else. It’s not merely an exercise in having a clear brand voice and direction to build from, though the foundational work gives you exactly that. The structure of everything you’re trying to build rests on whether your foundation can be read, resonated with, and trusted.

Now, it’s simply that there are two readers evaluating it. The stakes didn’t change the work. They clarified it.

The through-line is this: AI amplifies what’s already there.

A strong foundation—specific voice, consistent expertise, living values, content with a coherent through-line—reads as authoritative. What reads as authoritative gets referenced.

What gets referenced gets recommended.

A fragmented foundation—voice that shifts by channel, content that follows every trend, expertise that’s stated but never demonstrated—reads as noise. And AI moves past noise without ceremony. No note. No explanation. Just gone from consideration entirely.

This evaluation was never announced. There’s no algorithm update email, no notification that a new reader entered the picture. The AI is simply there, reading your business the same way it reads every other business, synthesizing a picture from the evidence you’ve laid down—and determining, in milliseconds, whether you have something coherent to offer.

🧐 Upon Reflection: The Work Has Always Been Strategic Infrastructure

The reframe I want to leave you with predates any specific AI strategy or discoverability tactic.

AI is an interpreter of foundation. It reads what you’ve built and translates it—into authority or ambiguity, recommendation or invisibility—before the human ever enters the picture.

Would it read a business with a clear, consistent, distinctive voice? A body of expertise that’s specific and coherent, that says something recognizable about what you do and who you do it for? A founder whose values show up the same way in a welcome email as they do in a thought leadership piece, whose messaging has a through-line that holds even as the topics change?

Or would it read something looser than that? A business that sounds a little different every few months, whose positioning has pivoted enough times that the edges blur when you try to nail it down? A founder whose content is good but whose brand identity is still more aspirational than actual?

The human reading your work might feel the intention underneath the inconsistency and stay anyway. The AI reads the inconsistency and moves on.

The good news—and I mean this—is that the answer to this question is the same answer it’s always been. It has not been made complicated by AI. Not really. If anything, it has been made clearer.

Build the foundation. Do the voice work. Get specific about your expertise, specific about your audience, specific about what you stand for and how that shows up across every single touchpoint.

Make the coherence intentional instead of accidental. Let the through-line be unmistakable.

This was always the work. It was always what separated the businesses that build compounding trust from the ones that stay stuck in the cycle of starting over. The business that sounds consistent—clearly, congruently, across every surface it occupies—is the business that humans want to work with and AI systems are willing to recommend.

The difference now is that there are two audiences evaluating your foundation instead of one. And one of them arrived before you even knew the evaluation had started.

When an AI reads everything you’ve put out into the world so far, what story will it tell about your business?

A clear voice tells a clear story. A consistent point of view tells a consistent story. A well-defined expertise with a coherent body of content behind it tells a trustworthy story.

And a trustworthy story is what gets recommended.

None of this calls for a rebrand. Rather, what it does call for is an audit of alignment—a clear-eyed look at whether your voice, your values, your language, and your claims actually agree with each other across every place a system might encounter them.

The founders who treat this moment seriously aren’t the ones racing to optimize for algorithms. They’re the ones using this shift as a forcing function for something that was always worth doing: building a business whose foundation is solid enough to survive being read by something that has no incentive to be generous about the gaps.

🤔 Your Turn To Reflect

Take a moment here before you move on. Everything this issue has named about the way AI reads your business—the voice, the values, the specificity, the coherence—it all points back to the same place: the foundation you’ve already been building. These questions are where you bring it home.

These aren’t audit questions—they’re places to bring honest attention.

➔ When did you last consider what your ecosystem signals to someone who has never heard of you? Pull back from what you intended to communicate and sit with what your content, bio, and structure are actually demonstrating together.

➔ Where in your ecosystem is there a gap between what you say you do and what your content proves you understand? Voice, values, specificity, consistency—which of these signals is the most fractured right now?

➔ If AI is reading your foundation before your audience does, what’s the one thing you most want it to find? And is that thing visible, specific, and demonstrated—or does it live mostly in your head?

One honest answer to any one of these is a beginning. The foundation gets clearer every time you’re willing to look at it directly.

🌀 The Ecosystem Pulse

Before you go—one honest question: Did this issue nourish something?

Reply with a single letter and let me know how this one feels for you:

A—Yes, I feel more aligned
B—I’m still sitting with it
C—This one didn’t connect

Every signal strengthens the ecosystem. Thank you for being part of it.

🧭 Constellation Compass

🏠 This week’s theme—AI Reads Your Business Before Your Audience Does—has been swirling across all corners of Sitting Pretty Strategies.

📍 In this issue of Pretty Strategic, I made the structural case for coherence as an AI-era indexing requirement—and why the founders who built foundation-first are the only ones a recommendation engine can surface with confidence. [Read PS #039 here ➔]

🔮 Inbox Alchemy is the lab side of SPS—where ideas like these get turned into practice through a sequential, evergreen email experience—the hands-on companion to everything SPS publishes, one issue at a time. If you’re ready to move from insight to implementation, [Join us in the lab →]