We have built something extraordinary, and we have already mistaken it for the destination.
Large language models are the most impressive cognitive tools humanity has ever produced. They read, write, reason, and argue with a fluency that still feels uncanny. But fluency is not intelligence. A concert pianist who is deaf can read a score perfectly and never hear music. That is roughly where we are with AI today, extraordinary facility with one channel, and a growing illusion that the channel is the whole.
The brain was never a language model. It is a fusion engine.
At every waking moment, your nervous system is ingesting dozens of simultaneous data streams, the angle of light through a window, the grain of a surface under your fingertips, the slight tilt in your inner ear that tells you the ground is uneven, the ambient audio that tells you a room is empty before you’ve consciously registered it. None of these streams is dominant. They are constantly weighted, cross-referenced, and collapsed into a single coherent model of where you are, what is happening, and what matters. You don’t experience the world one sense at a time. You experience it all at once, and the integration is the intelligence.
Large language models were not a mistake
The last three years of AI progress have been built almost entirely on text. This was not a mistake, language is the densest compression of human knowledge we have, and mining it has produced genuine miracles. But the field has begun to hallucinate that scaling text is the path to general intelligence. It is not. It is the path to a better autocomplete. A magnificent, occasionally breathtaking autocomplete, but autocomplete nonetheless.
The more interesting story, the one that is barely being told in mainstream financial and technology press is what is happening at the edges. Companies are training models on tabular data, building systems that extract signal from the structured numerical reality of the world rather than its linguistic surface. Others are training on video, learning the physics of how things move, fall, and interact in ways text can never fully encode.
Robotics firms are assembling machines with GPS, directional audio, and high-resolution visual sensors, systems that navigate physical space with more functional senses than a human soldier in a degraded environment. The resolution of these artificial senses will keep improving, and quickly.
The new AI architecture
What is emerging is not one smarter model. It is a new architecture: multiple specialised AI systems, each trained on a different sensory channel, each expert in its own modality, combining their outputs the way the human brain combines nerve signals from the eyes, ears, and hands. The fusion layer, the system that decides how to weight and integrate these streams in real time is where the real value will be created, and it barely exists yet.
The implications are not abstract. In defence, the military edge will belong to whoever can fuse battlefield sensor data: satellite imagery, acoustic signatures, electronic intelligence, biometric feeds, into a single coherent operational picture faster than the adversary.
In healthcare, diagnosis will cease to be the art of reading a chart and become the science of integrating genomic data, imaging, wearable biosensors, and behavioral patterns in real time.
In financial markets, the edge will shift from processing language — earnings calls, news, filings — toward reading the physical world directly: satellite data on port congestion, acoustic monitoring of factory activity, energy consumption as a proxy for economic output.
The question is no longer whether this happens. The trajectory is clear. The question is who builds the architecture that fuses it all together, and whether we understand what we’re building before it’s built.
Everyone is watching the language models get smarter. Almost no one is watching the world get legible.
Judah Taub is the founder and managing partner of Hetz Ventures, an Israeli early-stage venture capital firm specializing in cybersecurity, data, and AI infrastructure.
