I was standing in the cereal aisle a few weeks ago, doing the thing I always do — flipping the box over, scanning the fine print, comparing fiber grams like it mattered more than it probably does — when I thought about the model I’d been testing that morning. Sharp. Fast. Occasionally, confidently, wrong about something I could have looked up in ten seconds.
There was no label for that. No panel telling me what was inside, what it was good at, what it might get wrong, what it cost to run. Just a chat window and a kind of blind trust.
That’s the itch behind this post. What would it look like if AI models came with something like a Nutrition Facts label — the kind the FDA forced onto every box in your pantry back in 1994? Not as a gimmick, but as a real answer to a real problem: we are feeding these things into our decisions, our writing, our portfolios, our kids’ homework, largely on faith.
The IQ Number That Isn’t Quite an IQ Number
I keep running into a shorthand in investing circles — Jordi Visser and others talking about frontier models as “140 IQ” systems, reasoning at a level that outpaces most humans on the kinds of puzzles we associate with fluid intelligence. Pattern recognition. Logic chains. Novel deduction under pressure.
It’s a useful number. It’s also a bit of a trick.
Human IQ tests were built to measure something narrow and specific — not wisdom, not knowledge, not judgment, but the raw machinery of reasoning. When we borrow that language for AI, we inherit the same narrowness, which is fine as long as we remember it. A model that aces abstract reasoning benchmarks isn’t necessarily the model that knows the correct dosage, the right case law, or what actually happened in 1932. Reasoning and knowledge are cousins, not twins.
Two Kinds of Smart
Here’s an old-fashioned way to think about the split: Britannica versus World Book.
Britannica was the encyclopedia my father would have trusted — dense, expert-written, unapologetically deep, assuming you could keep up. World Book was the one actually sitting on the shelf in most houses I knew growing up, mine included: friendlier, broader, built for a general reader, a little shallower in exchange for being a little more useful on a Tuesday night with a homework assignment due.
Neither is wrong. They’re optimized for different things. And training data does the same kind of sorting. A model fed heavily on curated, scholarly, expert-vetted sources leans Britannica — deep, careful, occasionally slow to update. A model trained on the sprawl of the open web leans World Book — broad, current, occasionally sloppy, sometimes brilliant at the edges precisely because it’s seen everything.
Any honest label for a model needs a section on this. Call it “Knowledge Sourcing.” Not just how big the training set was, but what kind of encyclopedia it’s pretending to be.
Sketching the Label
If I could design the box myself, it might read something like this:
Serving Size: 1 query, ~500 tokens
Reasoning Score: 138 (fluid problem-solving, logic, abstraction) Knowledge Depth: Moderate–High (cutoff: [date]; strongest in [domains]; weakest in [domains])
Ingredients: Curated scholarly corpora, licensed news archives, public web crawl, synthetic reasoning data, human feedback Allergens: Confident hallucination under ambiguous prompts; recency gaps beyond training cutoff; known weakness in [specific domain]
Cost per Serving: $X per million tokens; Y watt-hours per query Best Paired With: Retrieval tools, human review for high-stakes decisions
It’s a little tongue-in-cheek written out like that. But underneath the joke is something I actually want — the same instinct that made me read cereal boxes as a kid. Not to be scared of what’s inside, just to know.
The Part That Actually Excites Me
Here’s where the scaling laws get interesting, and where I think the real opportunity sits.
World knowledge is expensive. It’s greedy for data and parameters — you need to have practically read the internet to know the boiling point of tungsten, the plot of a minor Victorian novel, and the org chart of a mid-cap company all at once. Reasoning, it turns out, is a different kind of animal. It can be distilled, compressed, taught through synthetic problems and careful post-training, and squeezed into something far smaller than you’d expect.
Which means a genuinely thrilling possibility is already taking shape: sharp, high-reasoning models small enough to run on a phone or a laptop, entirely offline, because they’ve shed the encyclopedia and kept the mind. Pair one of those with a personal index — your own notes, your own documents, a retrieval layer built around your actual life — and you get something closer to a personal thinking partner than a general-purpose oracle. Private. Fast. Always available. Tuned to you rather than to everyone. Apple may be on to something with this kind of strategy?
I think about this constantly in my own workflow — the daily scans, the little agents I’ve built to help sort signal from noise, the genealogy digging, the investment frameworks I keep refining. What I usually want isn’t more encyclopedia. It’s a clear-headed reasoner sitting next to my own carefully kept knowledge, not buried under someone else’s version of the whole internet.
Why the Label Matters More Than the Score
None of this works, though, without honesty about what’s inside the box. A 140 on a reasoning benchmark tells you almost nothing about whether a model will quietly misremember a fact it was never that confident about in the first place. And a model can be extraordinarily knowledgeable while being a mediocre reasoner — plenty capable of reciting the right ingredients and still getting the recipe wrong.
The nutrition label movement in food didn’t eliminate junk food. It just made it possible to choose junk food on purpose, with your eyes open, instead of by accident. I’d like the same deal with AI. Not a demand that every model be a genius generalist, but a demand that I get to know what I’m actually consuming — and choose the lean local thinker over the bloated encyclopedia when that’s what the moment calls for, or the other way around when it isn’t.
Curiosity got me into that cereal aisle habit decades ago, and it’s the same instinct pulling me toward this idea now — not suspicion of the box, just a wish to read it clearly before I decide how much of it to trust.
What would you want on your label?