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Ainos posted this CEO letter on its social media:
Why Smell Is Becoming AI’s Next Native Language
Building a Platform for AI to Understand the Physical World
From: Eddy Tsai, Chairman, President & CEO, Ainos, Inc.
For decades, AI has advanced by digitizing how humans see, hear, and read the world. Computer vision transformed images into data. Speech recognition transformed sound into tokens. Large language models transformed text into intelligence.
Yet one fundamental human sense has remained largely untouched by AI at scale: smell. I believe that is about to change.
Smell is one of humanity’s oldest and most instinctive forms of perception. It acts as an early warning system, a signal of safety or danger, quality or decay, normalcy or anomaly.
Despite its importance, smell has historically been difficult to digitize. Unlike images or sound, scent is highly contextual, sensitive to environment, time, materials, and subtle variations. Without consistency and structure, scent data cannot be meaningfully learned by AI.
Most legacy approaches to smell technology focus on detection—thresholds, alerts, or isolated signals. But detection alone does not create intelligence.
True AI requires context, comparison, and learning across environments and time.
At Ainos, we made a deliberate decision early on: we would not build a single application or vertical solution. Instead, we designed a layered SmellTech platform, purpose-built to scale over the long term.
At the foundation, Ainos operates at the edge of the physical world, designing and deploying AI Nose sensing systems that generate reliable, repeatable, and comparable scent data across real-world environments.
Above that foundation operates our wholly owned AI software subsidiary, ScentAI Inc. [\#ScentAI](https://x.com/hashtag/ScentAI?src=hashtag_click), which focuses exclusively on transforming scent data into AI-native intelligence. This separation is intentional.
At the core of our intelligence layer is what we call the Smell Language Model (SLM). SLM is designed to classify, contextualize, and learn scent patterns across environments—much like language models do for text.
Through this approach, scent is no longer just a signal. It becomes a language AI can understand.
In my view, semiconductors, robotics, automation, or smart manufacturing are not merely our markets - they are our first classrooms.
Platforms are defined by the capabilities they unlock over time, not by their first use cases.
As deployments expand, data volume grows, diversity increases, models improve, and intelligence deepens. This creates a natural flywheel—where sensing, data, and learning reinforce each other.
This is how intelligence compounds.
The future of AI will be defined not only by better models in the cloud, but by how well AI understands the physical world.
Sight, sound, language—and now, smell.
Smell → Data → Language → Intelligence
This is how AI learns to understand the real world.