Space Review | AINFT fully launches AI service platform, building the next generation AI infrastructure with "flexible aggregated payment"

When artificial intelligence evolves from a stunning technological breakthrough to an "efficiency tool" that permeates daily life, its usage costs have quietly become expensive. Behind this shift lies not only the maturation of business models but also a reflection that AI is accelerating towards a critical phase of becoming "digital infrastructure." When tools become necessities, their cost structure, selection methods, and long-term sustainability become unavoidable realities for every ordinary user.

Against the backdrop of rising AI usage costs, the AINFT AI service platform within the TRON ecosystem has recently been fully launched. This platform integrates top models like ChatGPT, Claude, and Gemini, providing a unified dialogue and API interface, and is deeply integrated with the TronLink wallet, supporting one-click login and on-chain payments. Its significant advantage lies in the fact that new users can use AI model services for free, while also supporting small recharges to earn points for paid services, with a 20% discount available for recharges made with NFT tokens.

This is not only a direct challenge to existing paid models but also raises a key question: as the trend of multiple models coexisting becomes prevalent, do users still need to bear high costs for a single model? The emergence of AINFT may point towards a more flexible and sustainable future for AI usage. This issue of SunFlash's roundtable gathered several industry observers and practitioners to discuss not the capabilities of models but the underlying logic of rising costs, examining how ordinary users should establish long-term effective usage strategies in the current context of AI becoming a high-frequency tool.

As AI Demand Becomes Diverse, Traditional Subscription Models Have Become Users' "Efficiency Shackles"

As artificial intelligence rapidly evolves from impressive technological demonstrations to indispensable daily "productivity tools" across various industries, a significant contradiction begins to emerge: on one hand, user demands are becoming unprecedentedly diverse and scenario-based; on the other hand, mainstream service models remain stuck in expensive subscription systems. This deepening mismatch between supply and demand makes traditional subscription models not only unable to meet flexible and variable actual needs but also transforms into an "efficiency shackle" that users pursue for high performance due to high fixed costs. In this SunFlash roundtable discussion, several guests analyzed the deep logic behind the continuous rise in AI usage costs from different dimensions, including technological evolution, market supply and demand, and user behavior.

YOMIRGO pointed out that the rise in AI usage costs reflects two core trends: first, AI tasks have evolved from early single-question-and-answer models to a "thinking chain" process for solving complex problems, involving multiple rounds of tool invocation and reflection, leading to exponential growth in computational consumption. As the requirements for output logic and quality increase, high-intensity reasoning capabilities have become the core cost item of AI services, driven by a large number of parameters, computational costs, and the global imbalance between high-performance chip supply and demand. Secondly, the continuous rise in AI usage costs also reflects that AI is increasingly becoming "infrastructure", integrating into daily workflows like water and electricity, becoming an indispensable pillar of productivity.

AISIM agreed and further emphasized that the rise in costs essentially marks AI's transition from an initial "novelty tool" to its current status as a "high-frequency productivity infrastructure." The deeper the user dependency, the higher the demands for various capabilities, and naturally, the underlying computing and operational costs rise accordingly. Grace explained from a user experience perspective that users are not only paying for better results but also bearing the costs of the invisible complex backend processes.

As the rise in usage costs becomes a definite trend, a more pressing question for users naturally arises: do existing payment models still fit the current demand landscape? In this regard, the roundtable guests reached a clear consensus: the traditional long-term subscription model that bundles a single model is clearly disconnected from users' real, variable, and increasingly refined usage scenarios.

Several guests pointed out from practical usage scenarios that user demands are essentially diverse and scenario-based. Both ONEONE and Grace mentioned that writing, programming, drawing, and other different tasks often correspond to the strengths of different models, and expecting a single model to maintain optimal performance across all fields is neither realistic nor economical. Web3 Monkey sharply pointed out that a large portion of the fees users pay for top models may be for that 20% of cutting-edge capabilities that are rarely used, while high-frequency demands can often be met by basic capabilities, leading to a significant imbalance in cost-effectiveness.

Furthermore, this model may limit users' choices and adaptability in a rapidly evolving market. YOMIRGO emphasized that AI technology is advancing rapidly, and long-term binding to a single model is akin to self-limiting, causing users to miss out on the technological benefits brought by the rapid evolution of other models. HiSeven also noted that users' core demands are shifting from "passively accepting fixed services" to "actively seeking optimal solutions," and they prefer to flexibly invoke the most suitable tools based on real-time needs rather than being bound by a single platform.

Ultimately, the discussion pointed to a clear conclusion: for a large number of ordinary users with dispersed demands and variable usage scenarios, the economic and practical viability of a model that requires long-term prepayment of high fees for low-frequency proprietary capabilities is facing severe challenges. AISIM summarized that more flexible and cost-effective service models have become a key demand that the market urgently needs to meet.

AINFT's Breakthrough Approach: Flexible Payments Restructuring Costs, Aggregated Entry Reshaping Experience

Faced with the structural contradiction between a single subscription model and diversified demands, the guests unanimously agreed that the answer to breaking this dilemma may point to a unified entry that can aggregate multiple model capabilities. The guests foresee that this is not just a simple aggregation at the technical level but will trigger profound changes from usage logic to industrial ecology.

Several guests pointed out that a unified entry will change the relationship between users and AI. AISIM described the current "faith disputes" among users regarding different models, stating that an aggregation platform will dissolve this unnecessary division, allowing users to shift from "paying for models" to "paying for problem-solving." HiSeven and ONEONE added from an efficiency perspective that this can greatly reduce the time and cognitive costs users spend switching, registering, and comparing across multiple platforms, making AI services as smooth as switching browser tabs.

Moreover, a deeper transformation lies in the complete evolution of task execution methods. As Grace metaphorically put it, in this new model, users will play the role of "general manager," no longer passively accepting outputs from a single tool but mastering the overall allocation rights, directing the most suitable "AI employees" to collaborate based on task characteristics. For example, one model is responsible for generating plans, while another model specializes in reviewing and optimizing; this teamwork greatly enhances the reliability and quality of work output.

This means that AI will shed its attribute as a specific tool and instead become a basic infrastructure akin to water and electricity: highly standardized, available on demand, and with precise metered payments. This goes beyond mere improvements in the user interface; it signifies a fundamental evolution of the entire AI service paradigm from a closed, rigid "tool provision" to a flexible, inclusive "capability opening."

As an example that aligns with this evolutionary direction, the AINFT platform within the TRON ecosystem is concretely realizing this paradigm shift. The participants analyzed its feasible paths to achieve the dual goals of "low cost" and "excellent experience" based on their personal usage experiences.

1. Revolution in Cost Structure: From Fixed Subscription to Flexible Payments

The guests believe that AINFT's core innovation lies in its economic model. Web3 Monkey analyzed its points system and small on-chain recharge mechanism in detail, which completely deconstructs the traditional fixed monthly fee model. For new users, the free points obtained through wallet login can meet basic needs. Currently, new users can receive 1 million points upon login, sufficient to cover daily low-frequency needs. For high-frequency users, the platform offers highly flexible recharge options, supporting multiple currencies such as NFT, TRX, USDD, USDT, and USD1, with a 20% discount available for recharges made with NFT tokens, allowing users to recharge as needed. Estimated monthly costs can be significantly reduced to the range of $5-15, achieving a transition from the burden of "fixed monthly fees" to "on-demand flexible payments."

2. Reshaping User Experience: Seamless Access and Sovereignty Control

In terms of experience, AINFT allows one-click login through the TronLink wallet, enabling immediate access to multi-model services. This design eliminates the tediousness of repeated registration and verification, seamlessly integrating AI services into the smooth experience of Web3. Additionally, the unified API interface provided by the platform allows users to flexibly embed this aggregated capability into their applications and workflows, greatly expanding practical boundaries.

3. Integration of Ecological Value: Sovereignty, Incentives, and Sustainability

At a deeper level, AINFT's model design reflects a focus on user sovereignty and long-term value. Its points and recharge mechanisms are not merely payment channels but a positive incentive loop: users who recharge with NFTs can receive additional rewards, allowing the costs of long-term participation and deep usage to be continuously optimized. This essentially returns choice and value rewards to users, aligning platform evolution with community interests, and building a more resilient and attractive sustainable ecology.

Faced with the rising costs of centralized AI services and limited choices, Web3-native solutions represented by AINFT provide a key breakthrough approach. It reconstructs the economic model through a points system and flexible payments, reshapes user experience through multi-model aggregation and one-click access, fundamentally utilizing the combinability and incentive design of blockchain to transform AI from a "closed subscription service" into an "open digital infrastructure."

As the core AI infrastructure of the TRON ecosystem, AINFT's practice goes beyond mere technical aggregation; it is attempting to build a new paradigm where AI agents can collaborate and incentivize within a circular system. This signifies a future where users no longer have to pay for a single model but can participate as sovereign participants in a more inclusive, efficient, and community-driven evolving intelligent digital ecology.