On July 20, 2026, ChinaSoft International announced the signing of a cooperation agreement with Beijing Dark Side Technology Co., Ltd., named the "Moon Landing Project," focusing on comprehensive and in-depth cooperation in the enterprise service sector around Agentic AI. This collaboration adopts a billing and joint innovation model centered on Token distribution, aiming to bridge the "final mile" of large model commercialization. Within this framework, service income is no longer simply priced by project, manpower, or traditional licensing but is linked to model call volume. Through continuous Token consumption and revenue sharing, both parties' interests are locked in, transforming a traditional Hong Kong stock IT service provider into a "high professional value Token operator" — it not only delivers systems but also operates a Token pool tied to intelligent computing power. Due to strict regulation on cryptocurrency trading and token financing within China, companies must clarify the technical billing attributes when using the concept of "Token" in public documents. Therefore, the "Moon Landing Project" becomes a rare compliant Token economy example: on one hand, it signals a new business model linking AI computing power and Token revenue to the capital market; on the other hand, it reshapes investors' perceptions that "Token = measurable production factor." This cognitive shift will directly influence global fund allocation between AI themes and crypto assets — as more traditional IT companies are seen as Token operators, traders may no longer view price fluctuations in isolation but instead price on-chain Tokens alongside off-chain model calls and enterprise-level Agentic AI cash flows, thus opening space for subsequent structural links among "computing power - Token - risk assets."

Moon Landing Project: Traditional IT Bets on Token Economy

For ChinaSoft International, the core change of the "Moon Landing Project" is not merely signing another large model project but shifting the business model from "selling project hours" to "selling Token consumption." The income logic of traditional IT services settles once per project cycle, person-days or maintenance contracts; whereas this collaboration with Dark Side specifically adopts a revenue-sharing model based on Token call volume, effectively slicing each inference of enterprise-level Agentic AI and every text generation into measurable "computing billing units," allowing the service provider to earn continuous revenue around these billing units. This revenue structure shifts ChinaSoft International from a one-time delivery contractor to a "traffic distributor" operating around Token usage, with its valuation narrative no longer focused solely on traditional IT cash flows but tied to an operational asset linked to the usage curve of large model Tokens.

Token sharing also rewrites the way value is distributed in the AI industry chain. In the past, large model companies priced through technology, and integrators earned packaged project fees, both parties negotiated premiums within single contracts; now, with profit sharing based on Token consumption, Dark Side masters "Token issuance and pricing rights" through model capability, while ChinaSoft International gains control over "Token circulation and consumption rights" through scenario implementation and enterprise penetration. The two parties resemble contractual partners on a chain: one responsible for minting and standards, the other for traffic and calls, with profits segmented based on Token usage. This structure parallels the ecosystem division around Token rates in the crypto world, where futures usage rights are financialized in advance and revenue sharing binds participants' incentives. The difference is that within China, strict regulations distinguish technical Tokens from financial tokens in trading and financing, thus here Tokens are still positioned as technical billing units, yet they still form a new "valued right" on the enterprise's books, reserving an interface for subsequent connections with digital asset narratives.

On the enterprise service side, ChinaSoft International serves as the hub for large client processes and systems. When it begins operating Tokens around Agentic AI, it effectively builds a routing layer among "computing power - processes - funds" internally. Agentic AI can penetrate finance, supply chain, and even asset management phases, and Token billing allows every invocation to map to costs and potential revenues, which enables ChinaSoft to be a comprehensive entrance for both AI usage data and enterprise financial data. Once the global market continues to strengthen the trading framework of "AI and digital asset risk preference resonance," the Token operation data generated around the Moon Landing Project may be viewed by the capital market as significant auxiliary signals when pricing risk assets like BTC and ETH: an increase in Token consumption interpreted as a proxy for computing power demand and the prosperity of the digital economy will inversely influence the funding rhythm between tech stocks and on-chain assets. Under the premise that Chinese regulations clearly differentiate technical Tokens from financial tokens, whether ChinaSoft can connect this set of compliant enterprise-level Token billing with the sentiments and structures of international digital asset trading will determine whether the Moon Landing Project is merely a billing model innovation or the starting point for the Chinese enterprise version of the Token economy to truly access the global digital asset pricing system.

Chinese AI Moon Landing: Regulation and Token Narrative

In China, there is a distinctly different regulatory sensitivity to the term "Token" compared to the global landscape. Domestic cryptocurrency trading and token financing have long been under strict regulation, with any structures approaching public fundraising, contraposition trading, or price speculation naturally treading the line of compliance. Therefore, this Moon Landing Project deliberately confines Tokens as "technical measurement units for large model call billing and profit sharing," with ChinaSoft International’s announcement focusing solely on enterprise services and commercialization without mentioning any public chain issuance or crypto trading arrangements. This is both a response to regulatory realities and a precise delineation of narrative boundaries. Under existing compliance guidance, domestic internet and tech companies usually strictly distinguish between internal points, computing power points, billing units, and tokens with financial attributes, preventing misinterpretation as disguised ICOs or off-platform crypto trading.

What is subtly intriguing is that this enterprise-level Token billing and sharing model naturally carries the shadow of "economic contracts," yet is wrapped in technical and operational language. ChinaSoft International positions itself as a "high professional value Token operator," emphasizing within the regulatory framework that the Tokens are measurement units for computing power and services; on the other hand, it projects a prototype of a "compliant Token economy" in the capital market and industry discourse, encoding revenue distribution, incentive binding, and demand fluctuations into a measurable unit. For domestic participants, this narrative may reshape their overall perceptions of on-chain assets and the Token economy — gradually transitioning from "untouchable high-risk financial instruments" to "a digital rights structure that can be designed, regulated, and integrated into real business processes." Once this cognitive migration occurs, even if the Moon Landing Project itself does not touch upon Bitcoin, Ethereum, or on-chain assets, the enterprise-level Token model will open new imaginative space for how Chinese funds view global digital asset pricing in terms of risk preference and capital allocation logic.

Agentic AI: Interface from Processes to Asset Allocation

The Moon Landing Project targets Agentic AI in the enterprise service realm — an intelligent agent capable of autonomous perception, planning, and task execution. Within ChinaSoft International's existing landscape of government and enterprise clients, it will first be embedded in seemingly "secure" process segments: approval flows, procurement, production scheduling, compliance checks, etc., automating past human judgments into accountable algorithmic decisions. However, the real leverage processes within enterprises often focus on finance and asset management modules: budget allocation, fund dispatching, term structure arrangements, external payments, and settlements, which are inherently natural scenarios for process automation and algorithmic decision-making. Once the Agent evolves from assisting with reporting and monitoring indicators to providing recommendations on "how to allocate funds," it quietly shifts from being a process tool to a "funding interface" closely related to cash flow.

ChinaSoft International's experience in delivering solutions in typical scenarios such as finance and manufacturing implies that the Moon Landing Project has the ability to directly penetrate these high-value modules, even if the current protocol does not disclose specific industries and functions. Tracing the internal evolutionary logic of enterprises, Agent will first comprehend various Token billing data and cost structures after integrating with financial and investment systems, then start balancing risks and returns among different asset pools. Against the backdrop of repeated risk preference resonance between AI themes and crypto assets in the global market, this balancing will sooner or later regard on-chain funds and risk assets like Bitcoin and Ethereum as "optional factors," while the truly observable variable is whether the enterprise-level Agent is granted the authority to make final decisions on the flow of funds and asset allocation, as this will determine the actual impact of the Moon Landing Project on the global pricing of mainstream crypto asset risks.

Computing Power Premium and Risk Asset Linkage

The Moon Landing Project embeds the dependence of model companies like Dark Side on GPU computing power and data resources directly into the business structure of a traditional IT service provider: computing power and data are no longer merely cost items but are incorporated into the revenue distribution logic through the Token sharing model, becoming "productive factors" that can be measured, traded, and hedged. At a global narrative level, this resonates with some institutions’ perception of "Bitcoin as digital energy, as a computing power asset" — whether training models such as Kimi or maintaining the security of the Bitcoin network requires continuous investment in high-density computing power. The logic of assetizing computing power premiums serves as a bridge for these two narratives to resonate.

This bridge is not an abstract concept in the global market. Historically, the US stock AI sector and crypto assets have seen capital rotations and resonance during multiple phases of increased risk preference, with tech growth and high-volatility risk assets viewed as different "leverage" within the same risk factor bucket. As ChinaSoft International moves from traditional IT service providers towards high professional value operators around the Token economy, it automatically enters the radar of global tech theme funding and quantitative strategies: model call volume, Token consumption, and computing power input become new variables for these funds to evaluate corporate value and sector exposure. The chain reaction of corporate AI investment expansion in asset allocation entails funds initially leveraging equity and computing power, incorporating Bitcoin, Ethereum, and other assets regarded as "digital energy" into the same trading structure when risk preference rises further. This will transform part of the computing and data premium into risk premiums and revaluation points for these crypto assets. Whether computing power premiums are repriced as valuation factors for risk assets like Bitcoin and Ethereum will become a key observation point in the process of advancing the Moon Landing Project.

Crypto Observation Checklist in the Era of AI Revenue Sharing

The structural signals released by the Moon Landing Project indicate that traditional IT service providers and AI-native companies are rewriting "computing consumption" into "distributable revenue" through Token sharing, and shifting service providers from one-time project delivery to a long-term relationship of ongoing revenue sharing based on model call volume. This provides a replicable enterprise-level template for the so-called "compliant Token economy." From a mid-term perspective, a reasonable trading hypothesis is: as long as this type of AI Token sharing model demonstrates considerable replicability in the Chinese enterprise service sector, global funds will repackage risk exposure under the theme of "AI + Token," incorporating AI stocks, computing assets, and Bitcoin, Ethereum, and other "digital energies" into the same narrative basket, leveraging during macro risk preference uplifting phases while on-chain funds will restructure trading structures around assets and protocols that can capture AI call volumes and enterprise-level Token billing metrics. To validate this hypothesis, it is essential to closely monitor three types of observation points: first, to see whether the Token sharing in the Moon Landing Project and joint innovation with Agentic AI are replicated by more large enterprises to form industry mainstream; second, to observe whether the regulatory attitude towards internal Token billing and revenue sharing remains stable and neutral, consciously delineating boundaries with financial tokens; third, to observe whether enterprise-level Agents extend along financial and asset management scenarios and scale from experimentation with interfaces for on-chain AI Agents — this entire set of observable variables will determine whether the Moon Landing Project is merely a company-level innovation event or the beginning of the underlying narrative for the next round of crypto asset risk preference.

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