One hour in a scanner is now enough to rebuild what you see
Today's inbox pairs two sides of the same question: what AI can infer about people, and what it takes to defend against it. A brain decoder that used to demand 40 hours in a scanner now needs one, and the founder of Mandiant banked a monster round for AI that attacks like a human team.
A brain decoder can now rebuild what you're looking at from a single hour of fMRI — and it learned mostly from images no one ever showed a subject. Michal Irani's team at the Weizmann Institute of Science split the job in two: one branch predicts the layout of an image (where the colors sit), another predicts its content (a bunch of bananas on a plate), and a diffusion model turns those predictions into a picture. To get enough training data, they also trained the system backward — an encoder that predicts brain activity from an image — so about 70% of the training corpus was never paired with a real scan at all. The payoff is calibration: where earlier decoders needed roughly 40 hours of fMRI on each new person, this one needs about one hour, which matters because scanner time runs $600 to $1,000 an hour and "none of us can afford 40 hours of imaging for a new subject," says Tommy Sprague of UC Santa Barbara, who was not involved in the work. The failures are vivid — a cake came back as three sandwiches, a dog in a bathtub as a goat — and despite the "mind-reading" label, it reconstructs what you see, not what you think; lead researcher Michal Irani says dreams and inner thoughts remain aspirational. That distinction is the whole story: the mental-privacy debate people are having about this result is running ahead of the actual capability, but the trajectory is real — her group is moving to video and audio next, and other labs are chasing the same decoder through EEG, where a cap (or headphones) replaces a $1,000-an-hour scanner. We covered that cheaper, cruder path earlier — AI decodes silent reading from a 19-electrode EEG headset.
Mandiant founder Kevin Mandia's AI-security startup Armadin raised $255.5 million at a valuation of more than $2.5 billion. The Series B, co-led by Andreessen Horowitz and Accel with Bain Capital Ventures and Redpoint coming in, lifts total funding to $445 million just seven months after the company's public launch — a pace that says investors are buying Mandia's framing that "AI lets an attacker find and chain weaknesses faster than any human team can respond." Armadin sells swarms of AI agents that behave like hackers and already runs production campaigns for Fortune 500 and government customers, which puts fresh capital on the offensive side of a security market that has spent the past year funding defensive trust layers instead. The take: at this multiple, the market has decided AI-driven offense is a category of its own — and that the fastest way to price it is to back the people who spent decades defending enterprises from the human version of the same playbook.
What to watch: whether the decoder survives the move from fMRI to EEG without collapsing — that is the step that turns a lab result into a mental-privacy problem.
If a one-hour scan let a locked-in person finally speak, would you accept the trade? Tell us in the comments.
Sources: MIT Technology Review · Weizmann Institute of Science · Jewish Chronicle · Reuters · SecurityWeek
