Triple Threat: Has the new AI made the old AI relevant again?
Latest Report: How to get real value from AI agents—without the hype.
Get The Guide<br>→
The Aboard Newsletter
Triple Threat
Has the new AI made the old AI relevant again?
by<br>Paul Ford<br>August 5, 2026
Finally, we can have this.
Richard MacManus, of ReadWriteWeb fame and now the editor of Latent.space (on Substack) has a great writeup on the return of the knowledge graph in a world of LLMs. As a big knowledge graph fan, I’ve noticed this too! To quote MacManus:
Perhaps ontologies are starting to resonate with AI engineers because a central concern at this time is quality control for loop engineering. We saw this debate play out at [The AI Engineer World’s Fair Conference], with many conference speakers not willing to go all-in on fully automated “software factories” just yet. One of the key learnings from the event was that there need to be guardrails and humans in the loop.
I want to offer an explanation, some caution, and a prediction, in that order.
Explaining Knowledge Graphs
First, let’s explain “knowledge graph,” as well as “ontology.” A knowledge graph is a database of facts, but the facts can link together (that’s the graph part). Here are five facts:
Julie isA person.
Julie isA productManager.
Julie worksFor GitHub.
GitHub isA company.
GitHub isOwnedBy Microsoft.
Those particular statements take the form of “triples”—Subject, Predicate, Object. It’s a tiny little database but nonetheless, we can go full Aristotle on it and derive other facts: A person can be a product manager; Julie ultimately works for Microsoft; and so forth. But what if I made a mistake and typed in, “GitHub isA productManager”? That would make no sense.
Want more of this?
The Aboard Newsletter from Paul Ford and Rich Ziade: Weekly insights, emerging trends, and tips on how to navigate the world of AI, software, and your career. Every week, totally free, right in your inbox.
So we need a way to express rules about facts, like “Product manager is a work role; work roles must be filled by people; and one person can have at most one role.” Then the computer could catch my error by checking my facts against those rules. It could use the rules to derive more facts, too. You could give a computer thousands of facts, apply rules, and get millions of facts as a result. What a bargain!
When you bundle up all the rules, that’s called an “ontology.” It’s the worst word imaginable for what it is, but you can’t stop nerds from being pretentious. An ontology lets you look at the facts in a knowledge graph and say “that’s a valid fact,” and, “since we know that fact, we know these other facts, too.”
Knowledge Is Hard
The big problem is that the world doesn’t come as a bundle of facts, so you have to make them—and make the ontologies, too. This can be a whole actual job. Decades ago, the machine reasoning company Cyc offered a role as “First Order Logic Axiomatizer.” That’s stuck with me. I really wanted to get that job and introduce myself as an axiomatizer at parties, just as soon as anyone invited me to a party.
If you want to see what this all looks like in practice, check out Wikidata. It’s a huge knowledge graph derived from the data inside of Wikipedia, built by humans, and used to power automated projects inside of the Wikipedia world—to support translation, for example. Here’s the entry for “knowledge graph,” to get recursive. Or the one for the author Douglas Adams, which is sort of their key example entry.
I am a big fan of knowledge graphs because they let you capture all kinds of relationships and they’re very “webby” because they link data together in loose ways with tools to explore the links. But—here’s the caution, as promised—there are lots of tradeoffs. As you can see, they’re conceptually challenging. A normal database lets you save a purchase order and later retrieve it. These are bundles of ideas, entities, and relationships and you explore them to find new ideas and relationships. They are hard to query because they don’t see the world as a set of rows and columns in tables, but as a huge bag of triples.
Connect For?
So why bother? Well, hit the open query endpoint for Wikidata and try out some of the examples. Here are some queries you can make:
List of torture devices
Most prolific fathers
Objects with most mass
Cathedrals in Paris
Rock bands that start with “M”
Sandwich ingredients
Ten largest islands in the world
I find this awesome. You could also ask for “most prolific fathers by nation” or “list of torture devices used in the medieval era.” You can keep connecting everything to everything.
But look at that last one. The query for the largest islands is as follows:
SELECT DISTINCT ?island ?islandLabel ?islandImage WHERE {<br>?island (wdt:P31/(wdt:P279*)) wd:Q23442.<br>OPTIONAL { ?island wdt:P18 ?islandImage. }<br>?island (p:P2046/psn:P2046/wikibase:quantityAmount) ?islandArea.<br>SERVICE wikibase:label { bd:serviceParam...