Tokens Built The Digital AI Business. Physical AI Needs A Different Unit

Brandon Barbello is co-founder and COO of Archetype AI, with over 10 years of product leadership across Fortune 500 and startups.

​Almost every industrial AI deployment I’ve seen lately eventually reaches the same meeting. The pilot worked, the operations team wants to expand to more machines and finance asks what the numbers look like at 300 assets instead of three.

Increasingly, that question comes with another: Will you charge us by the token?​

Enterprises have spent the past few years learning a new vocabulary around AI economics: tokens, inference, context windows, consumption-based pricing and discovering how hard those costs are to predict. Accenture recently reported that one in three organizations exhausts its AI token budget before year’s end.​

For digital AI, token economics are justifiable. Jensen Huang has said he’d be “deeply alarmed” if a $500,000 engineer didn’t consume at least $250,000 of tokens a year, because the spend buys output. For knowledge work, he’s right.​

But a compressor is not a $500,000 engineer. It produces signal every second it runs, regardless of whether anyone asks it a question. It makes more sense to analyze that signal locally, on hardware the customer already owns, than to ship every second of it to the cloud.​

Before Archetype AI, which builds physical AI for industrial customers, I led privacy-preserving, on-device AI projects at Google, where decisions about where intelligence runs were central to building practical products. That experience taught me that physical AI’s economics must account for the continuous flow of sensor data and the hardware already available to process it.

We have an opportunity to build business models that reflect those differences instead of importing LLM economics into the physical world.​

Physical AI is a different workload.

Most LLM interactions are episodic: A person asks a question or gives an agent a task, and the model produces an output.