General Sense was founded on the conviction that artificial intelligence has been built with a sensory blind spot, and that closing it is the most undervalued opportunity of the AI era. The progress of the last decade has run on two data modalities — language and vision — and both encode the world as humans happen to experience it. Cameras and microphones tell a machine what is visible or audible. Neither tells it what is in the air, in the water, on a person's breath, inside a shipping container, or growing in the soil.

The right way to think about what comes next is by analogy to what has already happened with vision. Computer vision and the models built on top of it have given machines a working physics engine of the real world: depth, motion, occlusion, object permanence, and scene structure, all decoded from photons. Autonomous vehicles, robotics, and the new generation of physical-world AI systems run on that engine today. None of those systems have a comparable engine for chemistry. The molecular world — which determines what is toxic, what is degrading, what is being trafficked, what is incipiently sick, and what is being chemically synthesized in plain sight — remains absent from every machine pipeline in operation.

We are building the first commercial system that decodes biological olfaction at scale and turns it into structured, machine-readable data. Longer term we want a sensory reasoning model: a foundation model for chemistry that learns the ontology of the molecular world the way large language models have learned the ontology of human language.

The sensor network and the model together are what we call the chemistry engine of the real world, a continuously updated, model-readable representation of the chemical state of any environment.

Every jump in AI capability has followed a new data modality arriving at scale. Text gave us large language models. Images gave us multimodal systems. Trajectories and actions are giving us robotics now. The frontier labs that drove that progress are openly looking for what comes next, because the scaling curves on text and vision are bending. The field needs new data, new modalities, and new ways to ground intelligence in physical reality.

Real-world chemistry is unclaimed. The physical world is molecular, and so are most of the threats that define this century: synthetic opioids designed faster than legislators can ban them, biological agents engineered in a single room, explosives at concentrations no electronic instrument can resolve, cancers that emit volatile signatures years before they are imaged. The visible world is well sensed. The molecular world is almost entirely unsensed, and machine intelligence cannot reason about what it cannot perceive.
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Make chemistry a first-class modality for machine intelligence and several things land at once. Sub-parts-per-trillion detection becomes continuous and ambient, outside the laboratory, in the places where it matters. Diagnostics go from episodic to constant. Biosecurity gets a sense it has never had. Customs, defense, agriculture, food safety, and environmental monitoring all gain a measurement regime that cost, latency, and the shortage of skilled operators currently gate.

It matters most for machine reasoning. A model trained on chemical signatures at scale starts to develop sensory semantics: it can identify objects, processes, and intent from molecular composition alone. A nurse who has just taken off nail polish and a chemist assembling a peroxide-based explosive leave overlapping traces. Telling them apart is a reasoning problem rather than a detection problem, and it is the kind of reasoning any system working in the real world will eventually have to do.

People ask why we are not putting this on robots. We do not think biological olfaction gets transplanted into autonomous machines any time soon, because the sensor is biological and biology is hard to copy. The knowledge coming out of it is another matter. What concentrations co-occur, what patterns predict what outcomes, what chemistry indicates what intent: that will change how robots, autonomous systems, and software agents understand the environments they work in and the tasks they are given.

A sensor with the sensitivity, speed, and adaptability this needs already exists, and it is not a machine. It is the canine olfactory system, refined over forty thousand years of co-evolution with humans and already working in our streets, ports, hospitals, and homes. The dog is not the theoretically optimal chemical sensor. It is the best sensor already integrated into our way of life, which means we do not have to solve the deployment problem. Somebody else solved it over forty thousand years.

The read-out is what is missing. Conventional canine workflows depend on individual handlers, individual animals, and behaviors that take months to shape and do not transfer between dogs.

We are replacing that bottleneck with a Nose-Computer Interface (NCI), a neural implant that records directly from the olfactory pathway, decodes the chemical world the animal is perceiving, and streams it as structured data into a model that keeps improving.

‍Every animal in the network feeds a shared chemical foundation, and every model improvement propagates back. What an isolated dog learns in months, the network learns once.

The next era of AI will not be won by training larger models on the same data. It will be won by giving machines access to the parts of reality they currently cannot see. Chemistry is the largest of those unclaimed modalities and the one with the most consequential applications: safety, health, and the basic problem of knowing what is actually in the world around you.


The endgame is broad, but in a nutshell, the plan sequentially follows a capital ladder:

1. Detect. Security pays us to find what's already being looked for.
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2. Predict. Agriculture pays us to find things before they're visible.

3. Discover. Health pays us to find signals nobody knew were there.

4. The chemistry engine. The ontology of how the molecular world behaves, and we get paid to build it.

Vision became the physics engine of the real world. Chemistry has no equivalent, because the right sensor has never existed on the machine side. Build it and you get machine intelligence that is no longer half blind to its own environment, and a molecular world it can finally reason about.