The future of artificial intelligence inference isn’t premium; it’s ubiquitous and commoditized.
That may sound odd coming from someone in the AI semiconductor business. Conventional wisdom suggests commoditization destroys value, so if AI inference were to become inexpensive and widely available, the market would shrink.
But history suggests otherwise. The technologies that reshape industries rarely remain scarce. Electricity, broadband, cloud computing and storage all followed the same path. As they became more affordable, more reliable, and easier to deploy, demand didn’t shrink. It exploded.
AI inference is approaching the same inflection point. The next phase of AI won’t be defined by preserving inference as a scarce, premium capability with high unit economics. It will be defined by driving the cost of inference low enough that organizations stop rationing its use and start embedding AI into everything they do.
This isn’t a race to the bottom. It’s the dawn of a larger market.
The luxury trap
AI is currently priced, marketed and deployed as a luxury product. The industry remains focused on scarce accelerators, premium systems, expensive deployments and extracting the maximum performance from every available resource.
This dynamic also limits adoption, encouraging enterprises to treat AI as a precious resource. 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.
For AI to become truly ubiquitous, organizations shouldn’t have to calculate whether another AI interaction is economically justified. While lower unit economics may appear threatening to an industry built around premium computing, the opposite is more likely to be true.
The $27 truffle and 99-cent chocolate
Legacy hardware providers fear that falling unit costs will shrink the total AI market. But lower inference costs also create new customers, workloads, and business models.
Imagine a gourmet chocolatier that sells handcrafted truffles for $27 each. The artisanal truffle commands a high margin, but its customer base is limited.
Compare that to a 99-cent chocolate bar. The market for chocolate doesn’t become smaller because the product is inexpensive; it expands because millions of people can afford it. That scale, in turn, supports entirely new products, distribution models and businesses.
As inference becomes more affordable, organizations that previously couldn’t justify significant AI deployments suddenly can. Existing AI services become more profitable because every improvement in inference efficiency reduces operating costs and improves the economics of applications already in production.
Companies can begin redesigning services around continuous AI usage – including ambient intelligence, autonomous systems, and always-on assistants – because the economics finally support running them at scale. Commoditization doesn’t reduce the value of inference; it allows inference to create value in far more places.
Beyond the digital dragster
Consider automobiles. A top-fuel dragster is a multi-million-dollar engineering marvel capable of extraordinary performance. But almost nobody wants to drive one to work every morning and no logistics company would build its delivery fleet around one.
The global economy runs on dependable, efficient, mass-market vehicles like the Toyota Camry and Ford Transit. They are affordable, easy to maintain, reliable at scale and designed for everyday use.
AI infrastructure must reach a similar point. A mass market for inference can’t be defined solely by which system produces the most impressive benchmark result under ideal conditions. Organizations care more about what a system can deliver consistently, economically and at scale. The question isn’t what’s fastest but what’s most productive and efficient.
Measuring outcomes, not activity
The metrics used to evaluate AI must evolve accordingly. Generated lines of code, requests per second and benchmark scores are useful engineering metrics, but they are not the same as business outcomes.
Businesses don’t invest in AI because they want to generate more code or tokens. They invest because they want to get more done. When inference becomes more affordable, organizations can spend less time optimizing every prompt and token and more time focusing on the work that AI makes possible.
Commoditization requires more than cheaper inference. It requires a shift in how the industry defines performance itself. Rather than measuring the activity level of a system in terms of throughput, the future measures of true business value ought to be focused on outcomes like task completion rates, business acceleration and time/money saved.
AI infrastructure for the global economy should be engineered to be part of everyday business operations. It should be affordable enough to deploy broadly, efficient enough to run continuously, and practical enough to integrate into existing server environments.
The real AI revolution begins when inference becomes so commonplace no one thinks twice about using it, not because inference has become less valuable, but because it has become valuable enough for everyone.
Marshall Choy is chief business officer at semiconductor firm Rebellions Inc. He wrote this article for SiliconANGLE.
