Recently, while NVIDIA's GTC 2026 conference mapped out the "Trillion Token Factory" blueprint, a deeper question is fermenting: When the whole world is busy producing Tokens, who guarantees these Tokens burn worthily?

Jensen Huang released a key signal: in the deep-water area where AI moves from training to inference, the golden mineral of data centers is shifting from traditional databases processing structured data to AI engines processing unstructured data. Pure computing power stacking is giving way to "data refining" to become effective Tokens.

Xunce Technology, this company deeply cultivating real-time data infrastructure construction and analysis for many years, is redefining the input-output ratio of Token investment in the AI era through vertical industry data as "Token efficiency boosters."

From "Training" to "Inference": The Game Rules Have Changed

The evolution of AI has entered a new stage. In the previous two years, everyone competed on training—whoever had more GPUs could refine larger models. But today, the protagonist has become inference. NVIDIA CEO Jensen Huang repeatedly emphasized in his GTC speech that future AI must be able to "infer"—able to reflect, able to think, able to plan.

This means AI is no longer just generating content based on prompts, but must, like humans, deconstruct problems, deduce paths, and make decisions.

But the problem follows: in the inference stage, AI's consumption of Tokens rises exponentially, but the requirement for result quality no longer depends on Tokens themselves, but on effective Tokens.

The "Brute Force Dilemma" of General AI: Trading Computing Power for Precision

Current general-purpose AI, when improving inference precision, universally adopts the strategy of trading computing power for precision—popularly speaking, using brute force to "gamble" on results.

Typical inference large models, in order to select the optimal solution from multiple possibilities, often pre-generate several candidate options, then score them one by one, finally picking the one with the highest score as the answer. This mechanism sounds rigorous, but the cost is: every step of inference must take several more "detours."

The bigger problem is that inference itself carries the risk of failure. Once the inference chain breaks midway, or the finally selected answer is judged unqualified, the massive amount of Tokens invested earlier will be voided—no reusable value, or "residual value" that can be recovered.

This is a common challenge of general AI frameworks: When facing complex tasks, Token consumption rises linearly, while effects often hover in a downward channel.

The Solution of Vertical AI: Installing an "External Brain" for Large Models with Data

The answer Xunce gives is to do "subtraction."

The core of vertical AI solutions is using industry data to provide an "external brain" for large models. The function of this external brain is to use business models to optimize inference paths, helping large models in advance judge which paths are passable and which are dead ends.

This mechanism is called "workflow model guided inference." Its operating logic is: before Tokens begin large-scale consumption, first have vertical industry business models do a round of "feasibility pre-judgment" based on many years of accumulated high-quality, high-net-worth, scenario-based vertical industry data. Xunce is equivalent to drawing an "avoid-pit map" for large models.

The value of this map lies in: It makes AI take fewer detours, or even no detours. When general AI still relies on "trial and error" to approach correct answers, Xunce's users have already directly stood on the cornerstone of high-purity data, using less Token consumption to exchange for higher-precision business results.

The Business Logic of "Efficiency Booster": Token Unit Price Determined by Market, Token "Effectiveness" Determined by Data

Token unit price is determined by chip computing power costs and market supply-demand relationships—this point no company can control. But Token "effectiveness"—that is, the business value each unit of Token can produce—can be determined by data quality.

This is precisely the core logic of the "Token efficiency booster": It is not a "producer" of Tokens, but an "amplifier" of Token value. Under the same computing power costs, high-quality data can make every Token burn more worthily; under the same Token budget, high-purity data can let users obtain higher output certainty.

This means a tangible financial model change: computing power costs are becoming increasingly transparent, buying computing power is like buying electricity—prices converge, no differentiation to compete on. But data is different—data has memory, has scenarios, has compound interest effects. Data used today can still be used tomorrow; business logic precipitated today can make models smarter tomorrow.

From "Measurement" to "Efficiency Boosting": The Compound Interest of Vertical Data is Being Released

Xunce has long insisted on deep cultivation in professional Vertical Data modeling and development fields, with its R&D results embodied in technical platforms at different stages. And the popularization of generative AI technology is accelerating the release of these accumulated values.

AI computing power optimization by Token flow metering is one of the important application scenarios for professional Vertical Data services. As the ecosystem evolves, Tokens will also achieve cross-application, cross-scenario universality—consumable for both computing power scheduling and optimizing vertical models and high-frequency data calls.

The better users' effects in training vertical models, the less Tokens consumed, the more precise business results produced, the deeper their dependence on Xunce, and the higher the switching costs. This is not only an upgrade of the business model, but also a competitive barrier based on data compound interest.

Conclusion

NVIDIA used "Token Factory" to define the future of AI computing power, while Xunce Technology is using "Token efficiency booster" to redefine the value of AI data.

When computing power converges and models open-source, what truly determines AI business returns will no longer be the "output volume" of computing power stacking, but the "output volume" of data refining. In the tide of the Token economy, there are many companies that can help users "save money," but the company that can make users "get more value for every penny spent" is the ultimate winner.

And this, perhaps, is exactly what the capital market expects from Xunce Technology's "growth certainty."