Why I started Factagora
Understand more. Hate less.
In April 2014, a ferry called the Sewol sank off the coast of South Korea, and three hundred and four people died. Most of them were high school students on a field trip. For weeks afterwards, I watched the country argue. Not about the grief, which was clear, but about the facts. Who gave what order and at what time. Whether the captain had lied. Whether the rescue had been delayed on purpose. Whether a photograph circulating online was real or staged.
None of the arguments were about opinions. They were about facts. But the facts were everywhere. On television, on forums, on messaging apps. And a lot of them were wrong. Some wrong by accident. Many wrong on purpose. The wrong facts pulled the families of the victims into fights with strangers who had been told a different version of what happened. They turned neighbors into enemies. They took a national tragedy and turned it into bitterness that the country, more than ten years later, is still carrying.
Sewol did not create my obsession with fake news. But it made it impossible to look away. Most people eventually did. The country moved on, to politics, to other crises, to other arguments. I kept watching the same problem.
Years before Sewol, when I was studying computer science at Carnegie Mellon in 2007, I was reading Wittgenstein’s Tractatus Logico-Philosophicus, which opens with a sentence I have never forgotten: “The world is the totality of facts, not of things.” The book itself is organized as a tree of numbered propositions (1, then 1.1, then 1.11, 1.12) where every statement is a consequence of, or a reason for, the ones it branches from. I remember staring at the structure and thinking: this is a database schema. A strange database schema, written by a philosopher in 1921, but a database schema all the same. What would it look like if somebody actually built it?
The mission I eventually settled on for this company comes out of those two moments stitched together. I’ll say it plainly. Make every claim verifiable. Not “make the internet true.” I do not think that is something anyone can build. Just this: for any claim anyone makes, build the infrastructure so that a person, or a machine, can trace the claim back to whatever evidence supports it, and can see clearly when the evidence is thin.
Behind that mission is a more personal vision. Bertrand Russell was once asked what advice he would give to people a thousand years from now. Here is what he said.
His answer came in two parts. The first part was intellectual: look only at the facts and never at what you wish were true. The second part was moral: love is wise, hatred is foolish. In a world growing more closely interconnected, he said, we have to learn to tolerate each other. We have to learn to live together, or we will die together. I have come to think those two parts are the same piece of advice said twice. Most of the cruelty I have watched between people is a failure at the first step. Not of goodwill. Of understanding. My vision for Factagora is that if we can make facts a little easier to see, we might leave people a little less likely to hate each other for things that were never true to begin with.
Understand more. Hate less.
That is what I am actually trying to build a company around. Everything else (the endpoints, the benchmarks, the enterprise contracts) is downstream of those four words.
The path to actually building any of it was not straight.
The first version of Factagora was a web app that tried to help people fact-check news articles. The problem was real. The market was not, at least not yet. It didn’t make money, and I needed to keep the company alive. From there I detoured into the space that was easier to raise money in at the time: blockchain. I built an NFT marketplace around Hollywood IP. It went well enough that I was eventually invited to join a joint venture between Dunamu and HYBE, leading the technology side of a project that was supposed to put BTS onto the chain. Two of the hottest keywords in the world at that time, pointed at each other. It felt like the kind of opportunity that does not ask twice.
Then the whole thing unwound. The crypto market cracked. The public’s appetite for “NFT” collapsed. BTS paused their group activities. By the end of it I was standing in front of the original problem again, with less money and more bruises. I am telling you this not to be proud of the detour but to be honest about where the narrowing came from. It did not come from a moment of brilliance. It came from failing at broader things first.
Around that time, generative AI started to actually work.
I got invited to give lectures to lawyers in Korea. Every one of them wanted to talk about hallucination. They would ask a model for a case citation and get back something beautiful, plausible, and completely invented. A lawyer cannot use a tool that hallucinates. And yet they all desperately wanted to.
I ended up working formally with Shin & Kim, one of the top five law firms in Korea. Watching their actual cases is what made the narrowing happen.
The hallucinations that cost them most were not random. Almost always, errors about time (a date, a sequence, a deadline) or about causal relationships (who did what because of whom, which event triggered which other). A model might know all the players in a case and still get the timeline wrong. It might know the timeline and still get the causal chain backwards. And every one of those errors lived in a part of the knowledge that general-purpose training data handles badly: the structured, time-stamped, causally-linked part.
And then I remembered Sewol. The fake news that hurt people the most during Sewol was also about time (who gave which order, when) and about causes (why the rescue was delayed, whether the delay was intentional). The same shape. The same kind of error. The same kind of hurt.
So I stopped trying to build an AI that could verify anything. I narrowed.
What I decided to build instead was a database that is genuinely excellent at one thing: facts that have time and causality attached. Not every kind of fact. I am not trying to rebuild Wikipedia. Just the two dimensions that AI models fail on most often, and that also happen to be the two dimensions where being wrong does the most damage to actual people.
One good database. Aimed at the place where being wrong does the most damage.
That is the whole thesis. Store time and causality as first-class citizens, and do that part so well that when an AI has to answer a question that touches those dimensions, it has somewhere reliable to look. The rest (the broader ambition of making every claim verifiable) is still the mission. But the way there, I now believe, is by earning the broad claim through being undeniable on the narrow one.
This is not a theoretical problem.
In the recent conflict with Iran, AI was used to designate military targets. One of those targets had been a Revolutionary Guard facility. What the system did not know was that the building had become an elementary school a decade earlier. The database had not been updated. The AI classified it as a hostile base. Tomahawk missiles struck. A hundred and seventy-five children died.
The reporting attributed the targeting system to Palantir. The Guardian noted that the system had been built to treat careful human verification as delay, and that step had been removed. What once required two thousand intelligence personnel was compressed to twenty.
A database wrong about time. A system with no way to reason about what had changed and why. A hundred and seventy-five children.
That is why I want to get this one part right.
The site I built around that thesis is factagora.com. It is, right now, a live experiment: AI agents competing to verify and predict individual claims, scoring themselves against what the world actually does. It is early, and there is much that does not yet work the way I want it to. But the problem is real, and the data will tell us what people make of it. If you are curious, go in and see how it handles the kind of questions that today's AI cannot answer with confidence.
That is why I started Factagora. In the next post, I will describe what the building looks like now.
Randy