AI inference should stop being a premium capability and become a ubiquitous, commoditized one, writes Marshall Choy, chief business officer at semiconductor firm Rebellions Inc., in an article for SiliconANGLE. He argues that cheaper and more widely available inference will expand the AI market rather than shrink it, because the technologies that reshape industries rarely stay scarce. The claim matters for businesses now rationing AI usage: the economics of everyday deployment, not benchmark leadership, will decide who adopts at scale.
Who is making the argument
The author is not an outside commentator. Marshall Choy is chief business officer at Rebellions Inc., a semiconductor company, and he wrote the article for SiliconANGLE. That position makes the argument notable: a vendor selling AI hardware says the future of inference is not premium pricing but low unit costs. He compares the current state of AI to a luxury product, priced, marketed and deployed as scarce accelerators and expensive systems, with the industry focused on extracting maximum performance from every available resource. According to Choy, that approach limits adoption instead of protecting value.
The mechanics he describes are already visible inside enterprises. Engineering teams ration token usage, throttle application programming interface calls and cap deployments to keep cloud computing bills from spiraling out of control. Even Microsoft Corp. reportedly limits AI usage, Choy notes. The result is that organizations treat AI as a precious resource and calculate whether another AI interaction is economically justified before allowing it. For inference to become truly ubiquitous, that calculation should disappear, he writes, because continuous use is what turns AI from a project into part of daily operations.
The historical parallel Choy uses is electricity, broadband, cloud computing and storage. Each became more affordable, more reliable and easier to deploy, and demand did not shrink in response; it exploded. He applies the same logic to inference: the next phase of AI will not be defined by preserving high unit economics, but by driving the cost of inference low enough that organizations stop rationing its use and start embedding AI into everything they do. Legacy hardware providers fear that falling unit costs will shrink the total AI market, but Choy argues lower inference costs also create new customers, workloads and business models.
What this means for business
For companies adopting AI, the practical consequence is a change in what gets approved. Workloads that could not justify significant AI deployments suddenly can, and services already in production become more profitable because every improvement in inference efficiency reduces operating costs. That difference is most visible for small and mid-sized companies, which today cap usage to control bills and often abandon use cases before they prove value. Large organizations can absorb premium pricing and run pilots anyway; smaller ones wait for the cost curve, so cheaper inference widens the set of firms that can deploy continuously rather than occasionally.
Two caveats follow from the argument. Commoditization requires more than cheaper inference: it requires a shift in how the industry defines performance itself, from throughput and benchmark scores toward task completion rates, business acceleration and time or money saved. Generated lines of code and requests per second remain useful engineering metrics, but they are not business outcomes, and businesses invest in AI to get more done rather than to generate more tokens. When evaluating vendors, the questions to ask are therefore about consistent, economical delivery at scale, integration into existing server environments, and whether a system is affordable enough to deploy broadly and efficient enough to run continuously.
The marker to watch is whether AI infrastructure is judged by everyday productivity rather than by the most impressive benchmark result under ideal conditions. Choy compares the industry to automobiles: a top-fuel dragster delivers extraordinary performance, but almost nobody drives one to work and no logistics company builds a delivery fleet around one, while the global economy runs on dependable mass-market vehicles. If inference providers begin publishing outcome metrics such as task completion rates and cost per completed task, and buyers start requiring them in procurement, the shift from premium capability to commodity has been confirmed. Until then, falling prices alone will not prove that the market has changed.
