There is a technique in filmmaking called the Kuleshov Effect, and it is one of the most uncomfortable discoveries in the history of visual storytelling. Soviet filmmaker Lev Kuleshov intercut the same expressionless close-up of an actor's face with three different images: a bowl of soup, a woman in a coffin, a child playing. Audiences watching each version saw the actor expressing hunger, grief, and joy, respectively. The actor's face did not change. The emotion was entirely

We are not interacting with a mind. We are co-authoring the illusion of one, filling in the gaps the way an audience fills in the frames, and mistaking our own completion for the film.

Language models trained on text are working at maximum distance from the world they are supposed to understand.

The future of AI is not one enormous model that knows everything imperfectly. It is a network of specialists that know their domain precisely, coordinated by an architecture that understands how to deploy them.

These worries are valid, but the bubble is not in AI as a whole. It is in one specific bet: that endlessly scaling a single giant language model (or thin wrappers around one) will somehow deliver artificial general intelligence, profitable products, and transformative economics. That bet is showing deep cracks. Recent research backs this up. A 2025 analysis found that transformer models hit a hard mathematical ceiling on creativity: they can only remix past data, and they can

One of our team members just had research accepted as a Spotlight at NeurIPS 2025—the top 1% of submissions to machine learning's most prestigious conference. The work tests a provocative idea that's been circulating in the AI community: that all sufficiently large neural networks, regardless of architecture or training data, are converging toward the same internal representation of reality.

This post explains how we developed a system that detects five primary degradation types and routes each frame through the appropriate restoration model in real time, but more importantly it describes the insight that made the system practical: that weather and visibility change slowly enough that you don't need to classify every frame, just watch for transitions.

We're not trying to replace human judgment. We're trying to give analysts enough information and insights so their judgment isn’t blinded. When a security director asks "What happened?" we want the answer to be based on evidence, not guesswork.