Locally Smart, Globally Blind

Why I'm starting this — and the one idea underneath all of it

I came to artificial intelligence the long way around: through topology, the branch of mathematics concerned with shape, continuity, and how things fit together. For years, the questions that held me had nothing obviously to do with artificial intelligence. They were about a quieter, stranger problem — how local information assembles into a global whole, and what it means when it can’t.

It turns out that was the right preparation. Because one of the deepest problems in AI right now is exactly that problem, wearing a new costume.

Here is the observation this entire publication is built on. Take any intelligent system — a language model, a trading desk, a research team, a brain — and zoom in. Up close, every piece is locally competent. The model handles the paragraph in front of it. The analyst nails her slice. The neuron does its one small job. Each part, in its own corner, is sharp. And yet the system as a whole can be confused, contradictory, or wrong — not because any part failed, but because the parts never cohered. Locally smart, globally blind.

We have gotten extraordinarily good at the local part. The frontier models are astonishing in the small. What we have barely begun to take seriously is the global part: whether all that local brilliance actually glues into one trustworthy whole — and how you would even measure it if it didn’t.

That gap — between local competence and global coherence — is what I think about. It’s why I started building Sheaf, and it’s what I want to think about out loud, here.

Why “Local to Global”

In mathematics there’s a precise machinery for this. It’s called sheaf theory, and it studies exactly how data defined on the small, overlapping pieces of a space can — or fundamentally cannot — be stitched into something defined on the whole. It even gives you a way to quantify the obstruction: a number that says “these local views cannot be reconciled, and here is where the contradiction lives.”

I find that beautiful, and I think it’s badly needed in the realm of AI. And I don’t mean this as metaphor, but as a real lens. Most of what we call “orchestration” in AI is plumbing: routing messages between components and hoping. It answers: did the parts talk to each other? It does not answer the only question that matters in anything high-stakes: do the parts actually agree — and if not, exactly where, and how much?

So that’s one half of what you’ll find here: the technical thread. Multi-agent systems, multi-model reasoning, the consistency problem, and what it looks like to treat coherence as something you can compute rather than something you cross your fingers about. I’ll keep it rigorous, and I’ll keep it honest — including about what doesn’t work, which in my experience is where the real learning is.

The other half

But there’s a reason I didn’t call this publication something narrow and safe.

The same lens — local pieces, global whole, the seam where they meet — points straight at the questions that most people in AI either avoid or answer too quickly. What is thinking? Is there a real, non-mystical sense in which a system can be said to be conscious? When we say a model “understands,” what would have to be true for that to be more than a figure of speech? And the big one lurking behind all of them: what would it actually take for something we built to have a mind?

I don’t think these are questions you get to wave away as “just philosophy,” and I don’t think you get to answer them with a vibe either. My suspicion — and it’s only that, for now — is that a mind is not a single magic ingredient. It might be closer to what happens when many local faculties — perception, memory, reasoning, something like feeling — glue into a single coherent self that speaks in one voice. If that’s right, then “is it conscious?” may be a worse question than “how well, and in what way, does it cohere?” That’s the kind of reframing I want to chase here, carefully, in public.

I’m a builder, so I’ll be testing these ideas against things I actually make, not just arguing about them. And I’m a mathematician, so when I make a claim I’ll try to say precisely what I mean and where I might be wrong. I’d rather be interestingly uncertain than confidently empty.

What to expect

Two kinds of pieces, alternating: some technical and grounded (how this works, why it breaks, what the math really says), some philosophical and exploratory (mind, thinking, feeling, consciousness — taken seriously but not solemnly). The thread connecting them is always the same: local to global.

I’m not here to sell you certainty or to ride the hype cycle. I’m here to think hard about the most interesting questions of our moment, with whatever rigor I can bring, and to do it where you can argue back. The best thing that can happen is that you tell me where I’m wrong.

If that sounds like your kind of thing, come along. Subscribe, and I’ll write you the first real one soon.

— Jack