Why AI inference must become a commodity

The future of AI inference is not about exclusivity; it's about widespread accessibility and commoditization. This perspective, coming from an insider in the AI semiconductor industry, may seem counterintuitive. However, history shows that technologies that transform industries typically become cheaper, more reliable, and easier to implement.

As costs decrease, demand increases dramatically. AI inference is on the brink of this transition. The next stage of AI won't revolve around preserving inference as a premium, scarce offering. Instead, it will focus on driving the cost of inference down to a level where organizations no longer limit its use and start incorporating AI into every aspect of their operations.

This isn't about driving the cost down to the lowest price point, but rather about reaching a point where AI inference is as indispensable as electricity or broadband. Currently, AI is viewed as a luxury product, with companies focusing on high-end accelerators, expensive deployments, and maximizing performance. This approach limits adoption, leading enterprises to treat AI as a precious resource, rationing its usage and controlling deployments to manage costs.

Even major players like Microsoft reportedly impose limitations on AI usage. For AI to become truly ubiquitous, organizations must no longer think in terms of whether another AI interaction is economically justified. By lowering unit economics, we may actually be attracting more customers, workloads, and business models. Consider the difference between a gourmet, handcrafted truffle priced at $27 and a 99-cent chocolate bar.

While the truffle commands a high margin, its market is limited. In contrast, the chocolate bar expands the market as it becomes affordable to millions. As AI inference becomes more affordable, it opens up new opportunities for previously unprofitable deployments. Existing AI services can become more profitable due to reduced operating costs and improved economics.

Companies can start designing services around continuous AI usage, enabling ambient intelligence, autonomous systems, and always-on assistants that were previously uneconomical to run at scale. Commoditization doesn't diminish the value of inference; instead, it expands the areas where it can create value. Imagine the automotive industry.

A top-fuel dragster is a high-performance machine, but it's not designed for daily commuting or fleet operations. Instead, the global economy relies on mass-market vehicles like the Toyota Camry and Ford Transit, which are affordable, reliable, and easy to maintain. Similarly, AI infrastructure must reach a similar mass-market level.

The focus should shift from producing the fastest system under ideal conditions to delivering consistent, economically viable, and scalable performance. Businesses invest in AI because they aim to accomplish more, not just to generate more code or tokens. As inference becomes more affordable, organizations can shift their focus from optimizing every prompt and token to the actual work AI enables.

Commodity status requires more than just cheaper inference; it demands a change in how we measure performance. Instead of measuring throughput, future metrics should focus on outcomes such as task completion rates, business acceleration, and time/money saved. The AI infrastructure of the global economy should be designed to seamlessly integrate into everyday business operations.

It should be affordable enough to deploy widely, efficient enough to run continuously, and practical enough to fit into existing server environments. The real AI revolution will occur when inference becomes so common that its use is almost invisible, not because its value has decreased, but because it's valuable enough for everyone.

Marshall Choy, chief business officer at Rebellions Inc., a semiconductor firm, penned this article for SiliconANGLE.