Why AI inference must become a commodity

AI inference is heading toward the same place electricity, broadband, cloud and storage eventually went: cheaper, easier to use and too useful to stay scarce. That is the core argument in SiliconANGLE from Marshall Choy, chief business officer at semiconductor firm Rebellions Inc. And he’s pushing back on a pretty common fear in the AI hardware world: that lower prices mean a smaller market.

Right now, many companies still treat AI like a pricey resource to be rationed. Teams cap API calls, watch token usage and try not to let cloud bills get out of hand. The piece even says Microsoft reportedly limits AI usage. That behavior keeps AI on a short leash, which is fine if the goal is to protect margins, but terrible if the goal is to make AI part of daily work.

Choy’s view is that commoditization creates demand instead of killing it. He leans on a simple contrast: a $27 truffle versus a 99-cent chocolate bar. The cheap bar does not destroy the market for chocolate; it opens the door to far more buyers and, with them, new products and business models. He argues AI inference works the same way. When it gets inexpensive enough, companies that couldn’t justify heavy deployment can start using it, and systems already in production become more profitable because their operating costs fall.

That shift also changes what AI systems should be built for. The article says the future is not about top-fuel dragster style performance for its own sake. It is about dependable, efficient, mass-market tools like a Toyota Camry or Ford Transit: the kind of thing a business can run all day without a spreadsheet panic attack. That means judging AI less by benchmark scores and more by outcomes such as task completion, business acceleration and time or money saved.

The end point is blunt: AI matters most when nobody has to think twice about using it. Not because it is cheap and trivial, but because it has become useful enough to be everywhere.