From an industrial perspective, a Token is an asset; it has become a unit of revenue and profit.
AI companies will aim to build more Tokens, generate more Tokens, and produce more AI factories.
This core judgment was delivered by NVIDIA CEO Jensen Huang during a landmark keynote speech at Computex in Taipei on June 1st, which is set to be recorded in tech history.
In his nearly two-hour, extensive address, Huang's central thesis was that in the era of AI factories, every generated Token is a measurable unit of profit.
This defining metaphor for the coming decade stems from the dramatic transformation the concept of a Token has undergone within the industry over the past year.
Initially, it was merely a technical unit of measurement in the age of large language models, used to gauge input, output, and computational costs.
However, as generative AI moves towards scaled application and AI agents begin to manage longer, more complex tasks, the Token is acquiring an increasingly distinct "economic attribute."
The underlying shift in logic is that as the role of data centers transitions from "storage and computation" to "continuous intelligence production," a Token resembles a standardized part rolling off a factory assembly line.
Those who can produce and utilize Tokens with higher efficiency are closer to the core profit pools of the AI era.
Simultaneously, the Token economy is also gaining momentum in the Chinese market.
Public data indicates that China's daily Token calls have surpassed 140 trillion, representing a thousand-fold growth over two years.
The three major telecommunications operators also collectively launched Token-based service packages in May, with their stock prices subsequently rising consecutively.
Clearly, the Token economy is no longer confined within model developers but is beginning to spill over into a broader industrial phenomenon.
Yet, a critical question emerges: as Tokens become increasingly important, who are the most valuable recipients of these Tokens?
Token Value Lies in Conversion Rate, Not Consumption Volume
On the surface, the Token economy appears to primarily benefit model developers, computing platforms, and API aggregation service providers.
However, this is merely the upstream perspective of the industrial chain.
For investment and industrial analysis, the more crucial focus is often on the other end—which high-value scenarios ultimately receive these Tokens and achieve higher-quality commercial conversion.
This is because not all Token consumption is equally valuable.
Research suggests the value differential for Tokens across different scenarios can be as high as a hundred thousand times.
For instance, Tokens used in drug discovery can command prices around $1,000 per million Tokens, while those for casual conversation may be as low as $0.01 per million Tokens.
Therefore, the value of a Token is determined less by its production cost and more by its application.
A significant macro-trend is that the global AI industry has entered a new supercycle.
Analysts emphasize that the industry has formally transitioned from the first stage focused on computing power and foundational models to a second stage centered on enterprise applications, agentic AI, and scaled intelligent services.
Enterprise AI has moved from "generation" to "execution," where Tokens are the core cost and Agents are the core value.
Further predictions estimate that by 2026, the Token call volume in China's MaaS market will reach approximately 40,000 trillion, generating revenue of about 18.6 billion yuan, with a compound annual growth rate from 2024 to 2030 around 1154.9%.
Over 60% of leading Chinese enterprises have already integrated generative AI into their core business processes.
A similar view is held by other analysts, who identify AI infrastructure software as a key direction for securing predictable returns.
The recent surge of over 21% in a software index within a single month, driven by better-than-expected earnings from companies like Snowflake, MongoDB Inc., and Okta Inc., validates that the scaled deployment of AI is ushering in a new growth cycle for the software services layer.
If the production side of Tokens (computing and models) represents the "waterworks" and "factories" of the AI industry, then who consumes these Tokens, how they are consumed, and what value results from that consumption are the keys determining the ultimate valuation of the Token economy.
Within this context, AI marketing is particularly noteworthy.
It is being catalyzed simultaneously by the dual technological curves of generative AI and AI agents.
The continuous improvement in Token circulation efficiency will positively reinforce AI marketing from both the business-to-business and business-to-consumer ends.
It is also judged that the Token economy is likely to achieve scaled monetization first in three major fields: AI-generated content, marketing, and e-commerce.
Their common characteristics are: high-frequency demand, complete process chains, quantifiable results, and the ability for Token investment to quickly translate into metrics like click-through rates, conversion rates, gross merchandise value, and customer growth.
This explains why the overseas marketing sector is becoming an underrated position within the Token economy.
The journey of a brand entering a foreign market and establishing a growth loop is itself a lengthy industrial process from insight to conversion to brand building.
It typically involves multiple stages such as market analysis, audience identification, creative generation, content production, ad placement, performance optimization, and attribution analysis.
These stages are naturally suited for reconstruction by generative AI and agents.
In essence, this sector doesn't just handle sporadic Token calls; it manages a sustained, high-frequency, and scalable Token consumption system.
Within this system, the meaning of a Token also changes.
It is no longer just a cost item for model calls but becomes the "intelligent fuel" driving customer growth.
Of greater interest are scenario-specific Token value-added services.
Unlike standardized Token calls, these services are tailored to specific business scenarios—such as batch generation of multilingual ad copy, intelligent analysis of localized trending content, or bulk derivation of high-quality materials—involving meticulous orchestration and optimization of Token consumption.
This shifts from a volume-based "selling Tokens" model to providing "high-ROI, scenario-specific solutions," where every Token spent by a client corresponds to higher conversion rates and revenue performance.
In long-chain, high-complexity sectors like marketing, the premium potential of such AI-powered, scenario-specific value-added services has historically been significant.
Titan Tech, an AI marketing technology company that filed for a Hong Kong listing in February, has rapidly grown into an industry leader, driven by the high proportion of its AI marketing solutions.
In 2024, its revenue ranked first among China's local providers of AI marketing technology services for overseas expansion, with core metrics like business scale and client base leading the industry.
Models May Be in Surplus, but Demand Inlets Remain Scarce
In today's AI industry, model supply is becoming increasingly abundant, and the performance gap between top-tier models is narrowing.
However, genuine, sustained, and large-scale demand pools remain scarce.
This means the ultimate market competition is not just for model access, but for demand inlets.
Titan Tech holds such a critical asset.
On one hand, in 2025, Titan Tech served 100,000 advertisers, covering hot sectors like e-commerce, gaming, entertainment, and local services, indicating a consistently strong demand for Tokens.
On the other hand, its marketing multi-agent system, Navos, announced full integration with top-tier video and image generation models from ByteDance, Alibaba Cloud, and OpenAI in May.
Powered by its proprietary multimodal large model "Titanium" as a professional foundation, it handles multi-model, multi-task, and multi-stage intelligent calls within marketing scenarios.
This positions Titan Tech in a highly imaginative space: it possesses both a traffic pool for scaled Token consumption and industrial-grade scenarios that convert Tokens into tangible customer growth results.
The most scarce aspect of such companies is that they are not merely "selling" Tokens but are continuously enhancing the efficiency of Token usage and monetization.
In other words, Titan Tech functions more like a "demand-side amplifier" in the Token economy—leveraging real business scenarios to amplify upstream model capabilities into sustainable commercial value.
This is also the most significant variable to watch in the Agent era.
Jensen Huang repeatedly emphasized in his Taipei speech that the era of practical, functional agents has arrived.
The tripling of GitHub code submissions in the first few months of 2026 already demonstrates a significant release of productivity.
Compared to traditional generative AI, the key difference with an Agent is that it doesn't just generate content; it can complete a full cycle of "observation, reasoning, planning, tool calling, execution, and optimization" around a goal.
This means the primary consumption scenario for Tokens in the future will shift from single-turn interactions to increasingly long-chain, goal-oriented, result-driven workflows.
Marketing is one of the most quintessential examples.
For a brand expanding overseas, a truly effective AI system doesn't just help write a piece of copy or create an image.
It should understand different national markets, identify diverse cultural contexts, coordinate multimodal content production, dynamically optimize advertising placements, and continuously provide feedback and adjustments targeting conversion goals.
This is essentially an agentified task chain, and Titan Tech, having built its own marketing multi-agent system, is positioned at the core of this chain.
Therefore, the market's understanding of Titan Tech may need to evolve from an "AI marketing tool provider" to a "key platform in the Agent era connecting model capabilities, industrial scenarios, and customer growth outcomes."
Selling Traffic Faces Commoditization, Selling Growth Commands a Premium
As Tokens increasingly resemble a "new revenue unit," the most common industrial pitfall is mistaking "consuming more Tokens" for "creating more value."
However, true commercial competition has never depended on consumption itself, but on whether that consumption delivers results.
This is also the core criterion for judging the Token economy in its next phase: not who calls more, but who calls more valuably; not who is closest to the models, but who is closest to growth outcomes.
If we follow this line of reasoning, platforms like Titan Tech—which connect to a large-scale client base while possessing a professional model foundation and agent execution capabilities—indeed merit re-examination within a new valuation framework.
Because what it undertakes is not just Token flow, but the capability to convert Tokens into real business growth.
Jensen Huang recalibrated the measure of Tokens in the future economy in Taipei.
The question the market should truly be asking is: Who can ensure these Tokens are not merely generated, but are consumed most effectively, allocated most precisely, and ultimately transformed into customer growth, brand growth, and enterprise growth?
If the answer is "high-value industrial scenarios," then Titan Tech clearly occupies an increasingly important position.
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