Apple Gave Siri Hands

WWDC answered whether your assistant is private. It never answered whether it’s telling the truth — and Apple just gave it hands.

The smartest thing I’ve read about Apple’s WWDC didn’t come from Apple. It came from an analyst named Nate B. Jones, who watched the same keynote everyone else did and noticed that the real story wasn’t whether Siri had finally gotten smart. The real story, he argued, is a land grab over what he calls the trusted action surface — the place where AI actually meets your work, touches your apps, and is handed permission to do something. There are two great bottlenecks in AI, he points out: raw compute, which is Jensen Huang’s kingdom, and the trusted surface where intelligence becomes useful, […]

The Market Behind the Wall

Yesterday I told you what 2Brains is, and how it separates the saying from the knowing. Today, the part that ought to worry some very large companies: what all of it is worth if we’re right.

Wall Street is pricing the AI data-center buildout at something like $1.7 trillion by 2030. Almost all of that spend assumes one particular shape: vast halls of graphics chips answering questions by guessing, one likely word at a time. So ask the heretical question — how many of those “questions” are questions at all? How many are lookups? What’s our refund policy? What was Q3 revenue in the Ohio region? Is this patient allergic to penicillin? Those aren’t creative prompts. They’re retrievals, and an ordinary processor has answered retrievals […]

Two Brains

For two months this column has been describing an architecture. Here’s the part I kept in the footnotes: I’ve been building it.

I owe you a confession, and then I owe you a demonstration.

The confession first. For weeks I’ve written about why the machines can’t tell truth from plausibility — why detection isn’t a strategy, why fluency isn’t fidelity, why the only honest path is to separate the saying from the knowing and import truth from somewhere you can actually check. I’ve signed each of those columns with a one-line note that I co-founded a company “built on this conviction.” That little disclosure has been doing a lot of quiet work. These columns were not the musings of a neutral observer. They were the argument […]

GenAI is Fluent in Everything, but Faithful in Nothing

Why the machines hallucinate, why they have no worldview, and why truth has to come from somewhere else.

I’m going to say something that sounds like an insult and is meant as a description: large language models (all of them) hav never known a true thing. Not once. It doesn’t know things at all. It is extraordinarily good at sounding like it does, which is a different skill, and most of our present confusion comes from mistaking the second for the first.

Here is what a language model actually does. It has read an enormous amount of text, and from that text it has learned, with real brilliance, what tends to come next. Give it some words and it predicts the words likely to follow. That […]

Detection Is Not a Strategy

Every few weeks, someone announces a tool that detects AI hallucinations. A startup, a research lab, a hyperscaler bolting a “trust layer” onto its chatbot. The release uses the word “guardrails.” Everyone nods. Another brick in the road to safe, reliable AI.

I want to argue that we are cheering for the wrong thing — that hallucination detection, however clever, cannot be the strategy. It can be a backstop. It can be a monitor. It cannot be the plan. And the reason is older than computing.

Start with the trap at the center of the whole idea.

To catch a hallucination, your detector has to know the right answer. Sit with what that means. The original model produced a confident falsehood because it did not have the […]