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.
Tacit knowledge helps us notice.
Curiosity helps us pursue.
Collective institutions determine whether that pursuit becomes durable knowledge and added to the collective.
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.
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. 🦸
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.
🌿 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.
✨ 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. 💌
Incredible piece! It reinforced the idea of going slow and intentional with learning. There is such importance in the wriggling of the brain, the discomfort of “I don’t get it yet”.
One thing I’d like to ask though: how should entry level’s approach this though? I’m observing that now more than ever, entry-levels are pressured to “deliver, deliver, and deliver”. What should entry level professionals focus on then to balance the need for autonomy and curiosity with the requirements in a workplace that demands SPEED?