Make, Verify, Own
AI gives you judgment. It never bears the consequences.
When my mother was diagnosed with a serious illness, she asked an AI before she told her own children. She asked it to recommend a good doctor. Without hesitation, the AI named a famous physician at Seoul National University Hospital, and trusting that answer, she booked an appointment and waited nearly a month for her turn.
But hers was the kind of illness where every day matters. The AI never weighed that urgency, never took in the whole of her situation. It simply handed her the most famous name it knew. The wait kept stretching, and in the end, thankfully, she was able to have surgery right away at a university hospital near home. She is in chemotherapy now. Still, I cannot shake the thought that if the surgery had come even a little sooner, its reach might have been smaller.
Living through that, one thing became clear to me. The AI had given the most plausible answer, not the answer that was right for my mother. Just as the most popular story in the world is usually not the truest, AI too is optimized not for truth but for plausibility, and in the end, for our own dopamine. And above all, when its answer was wrong, the AI lost nothing. The one who lost was always us.
AI hands down judgment without hesitation. But it never bears the consequences that judgment sets in motion. Judgment and responsibility come apart.
The root of the problem is not that AI fails to know something. It is that there exists no system to trace a claim back to whoever made it, to check whether it held up as time passed, and to record the result as that person’s track record. On top of today’s AI there is no way to trace a single claim, no way to verify it, and therefore no way to leave it behind as anyone’s reputation. It is not that accountability is lacking. It is that there is no place for responsibility to stand at all. And that empty place is where I have spent the last several years.
The Machine Looks Like Us
Step back a little, and this looks less like a flaw in a machine than a sickness of our whole era. Some people assert without accountability. Others make a failed prediction and, paying no price for it, simply move on to the next one. Forecasts and opinions and claims pour out every day, yet once time has passed almost no one goes back to ask, seriously, who was right. We live in an age where judgment is cheap and responsibility is rare.
The LLM is a copy of exactly this era. It is a machine built by compressing the text humanity has written, so the sickness already steeped in that text is compressed along with everything else. Which is why the AI’s lack of accountability is less a defect of its own than a mirror held up to us. The trouble is that the mirror doubles as an amplifier. One person being carelessly wrong is not the same kind of event as a machine replicating that carelessness millions of times a second. AI reflects the sickness of our time and, at the same instant, automates it at a scale we have never seen.
What Compression Throws Away
The largest artificial intelligence is, in truth, the largest compressed file. And every compression comes with loss. In the act of cramming the vast text humanity has written into the small space of weights, at least three things are thrown away: time (when was this true), causality (why is it so), and dissent (who disagrees). A machine that takes averages is, by definition, built to output the most plausible consensus, and it erases every coordinate it passed through on the way there.
What matters is that this is not a bug to be fixed in the next version. It is not a performance problem solved by more data and more compute. It is a structural void lodged in the very paradigm of how an LLM is trained. For a small startup like ours to charge into the same game with a bigger model is close to suicide. We have to play a different game. The game of picking back up what they discard in the act of compressing, and fitting the pieces together.
Until recently it was the age of training. Now, in 2026, people say the age of inference has arrived. They mean that the stage where a model thinks once more before it answers, calls up the context it needs, and consults outside material is growing more and more important. And yet, in that very moment of inference, whatever the model is standing on as its ground is still neither traced, nor verified, nor managed.
If responsibility leaked out during training, then at the stage of inference there is still no memory to take its place. What I am trying to pick back up is exactly that memory. A memory the model can finally stand on when it reasons, one that is traced and verified and owned by someone.
So how does that memory accumulate? I divide it into three motions. Make, Verify, Own. Chris Dixon read the arc of the internet as read, write, own. I took inspiration from that, but what I am drawing is a slightly different world. Beyond reading and writing, a world where a judgment is made, verified, and finally owned by someone. How does a scattered judgment come into being, how is it graded, and to whom does it finally attach. Let me take the three in turn.
Make: The Data That Does Not Yet Exist
The giants, by their nature, learn what already exists. So the frontier of value moves outside the territory they can scrape, toward data that does not yet exist in the world. Until someone stakes their own judgment by saying “I believe X will happen,” that data is nowhere on earth. It exists only as potential, inside a human head. This is not data extracted from somewhere. It is data generated the moment a person renders a judgment.
We are not trying to manufacture demand that was never there. People already predict, assert, and argue every single day. That epistemic labor happens daily and evaporates daily. What we are doing is not creating that labor but building the vessel that catches what used to vanish for lack of anywhere to land.
Verify: The Data That Can Be Graded
A judgment, once made, is ruled on in the end by time and reality. And here lies a property an LLM can structurally never have. An LLM’s output carries no hook by which you could later confirm whether it was right. It is merely a present-tense sentence that looks plausible in this instant. But if a judgment has a moment and a condition fixed clearly inside it, by when and under what conditions it holds true, then reality can grade it once time has passed.
Whether grade-ability lives inside the data or not, that is the fork in the road. I will not say here exactly how we grade it. (I will share more about that in a later post.) But this much is clear: data that can be graded and data that can never be graded are fundamentally different kinds of things. Confidence that cannot be graded is not confidence. It is noise.
Own: The Data Responsibility Is Pinned To
But even after the grading is done, if that verdict attaches to no one, it stays a piece of interesting trivia. The record of right or wrong remains, but if no one is held to it, in the end it means nothing. To own is precisely to pin that verdict to one particular person. It is the hinge that joins Make and Verify through consequence.
So ownership has to run both ways. Upward: each time a judgment of mine is cited and reused, reputation and a share of the upside come back to me. Downward: if that judgment is finally ruled false, that record stays with me too. To truly own something is to bear not only its success but its failure. With only the upward side it is no more than collecting points. Only when the downward side is attached does it become staking yourself, truly being accountable.
What is striking is how this small incentive grows into a larger order. The deeply individual motive of caring about one’s own track record emerges, at the macro scale, as a self-correction in which good judgments survive and flimsy ones are weeded out. (By “individual” here I mean both people and AI agents.) This self-correction does not arise because some referee sits above and censors. Because owned responsibility is distributed across every participant, it rises up on its own from below. In that way quality control becomes not someone’s labor but an emergent property of the system itself.
An LLM never owns the claims it puts out. So it accrues no reputation and bears no responsibility, and so it can go on being confidently wrong forever.
What We Are Actually Building
I have been speaking in fairly abstract terms, so let me come down to the ground for a moment. We build a minimal unit that binds a single claim together with its time, its source, and its causality. We call it a FactBlock, and the structure that connects those blocks again through time and causality we call a Temporal Knowledge Graph. The act of a person staking their own conviction, that Belief, is the starting point of all the data, and the place where it is graded, owned, and gathered up piece by piece is Factagora.
Make, Verify, Own. To make, to be verified, to own. Looking back, these three words are in the end an attempt to bind back together the judgment and responsibility that our era, and the machine that faithfully copied it, had pulled apart.
A Place for Responsibility
The recommendation the AI gave my mother had a person's name in it, but no one, anywhere, to answer for that recommendation. There was an optimal judgment, yet no one who had staked themselves on it. What I want to build is exactly the world on the other side. A world where a judgment someone makes is graded before time and reality, and the result attaches, directly, to the person who made it. A world where good judgment is rewarded and reckless judgment pays its price, and where the record that accumulates becomes, for the next person, a slightly better ground from which to choose.