Space Review | AINFT reshapes AI experiences with Web3 infrastructure, creating an open and autonomous productivity gateway

AINFT Launches AI Aggregation Platform to Address Large Model Subscription Pain Points, Supporting On-Chain Pay-Per-Use. New Users Receive One Million Points Upon Signup, Making AI Tools Easily Accessible.

Markets fluctuate repeatedly, narratives shift accordingly, but one area consistently maintains strong discussion momentum and user growth: artificial intelligence (AI). Unlike in the past, the current AI boom is shedding its conceptual halo and gradually solidifying into a tangible productivity force. More and more users are trying to use AI to assist work, spark inspiration, and handle daily tasks. Behind this trend is not only technological maturity but also AI moving from “being admired” to “being used.” However, technical implementation often comes with high costs and model selection difficulties, creating multiple barriers for ordinary users to experience AI firsthand.

Against this backdrop, AINFT is leveraging Web3 infrastructure to address these issues, officially launching an AI aggregation platform. It doesn’t tell grand disruptive stories but focuses on a more specific mission: how to innovate mechanisms and user experience to free powerful AI capabilities from cumbersome constraints, transforming them into more controllable, smoother, and trustworthy daily companions, thereby fostering an open and autonomous new ecosystem.

Based on this, a discussion centered on “how ordinary users can truly make good use of AI” has emerged. This episode, jointly hosted by Sun Wukong Ecosystem and AINFT in an X Space roundtable, starts from the perspective of ordinary users and invites industry KOLs to explore: why AI is once again becoming a main theme amid market volatility, and how AINFT’s innovative mechanisms—such as “wallet as account,” free trials, multi-model integration, and on-chain micro-payments—can realize the inclusive vision of “plug-and-play” AI tools. Below is a精彩回顾 of this conversation.

From Capital Narratives to Value Applications: Why Can AI Lead the Main Market in Volatile Conditions?

In the context of recent market caution and capital pressure, attention to the AI sector has actually increased. Several guests analyze the core logic behind this phenomenon from different angles, generally believing it’s not just a superficial market hype cycle but driven by deeper structural changes.

First, regarding market attention, AI’s “certainty” replaces “imagination space.” Multiple guests point out that the current market environment is experiencing a “truth-seeking” phase. Mr. Mis believes that projects relying solely on narratives are unsustainable, while AI demonstrates real empowerment for enterprises to reduce costs and improve efficiency, making it a value anchor capable of crossing cycles. Anna Tangyuan further provides key evidence from the user side: AI already has a large base of real users, deeply integrated into daily scenarios from learning to working. The market’s choices are becoming increasingly practical, and capital naturally flows toward fields capable of “self-sustaining growth.”

Crypto analyst Peter and Mo Yu add from a capital logic perspective that regardless of bull or bear markets, capital always chases the most creative directions. AI attracts “smart money” seeking long-term value, and the investment trends of leading institutions along with the popularity of consumer-grade AI products jointly boost market confidence in long-term allocation to AI tracks.

Focusing on the track itself, guests highlight three fundamental differences compared to the previous narrative:

From “single-point tools” to “workflows”: HiSeven accurately summarizes this as a shift from “watching AI” to “using AI.” AI is no longer an app to open separately but is embedded like water and electricity into various software and processes.

From “model competition” to “ecosystem integration”: Mr. Mis points out that the industry is moving toward infrastructure and protocol development, such as MCP (Model Context Protocol), standardizing how AI models and Web3 tools can be assembled like Lego blocks, greatly enhancing ecosystem composability. Mo Yu emphasizes: “AI functionality centralization and platformization are clear trends. Users need an integrated platform capable of handling multi-modal tasks, not multiple isolated tools.”

From “hype targets” to “value creation”: Niuiu mentions that the way AI makes money has changed; market focus has shifted from token prices to whether it can truly generate productivity and shorten work cycles. Practical applications like AI writing, coding, design, and financial analysis make it a measurable partner in investment returns.

Overall, the revival of AI hinges on its successful transition from “concept narrative” to “application landing.” It’s not only a promising investment track but also a real tool reshaping workflows and business models. This “application certainty” forms a unique and solid appeal in complex market environments.

Aggregation Gateway, On-Chain Payments: AINFT Builds Seamless AI Experience with Web3

As AI capabilities grow stronger, the gap between ordinary users and AI remains significant: complicated registration, rigid subscriptions, scattered tools, and daunting payment processes. In the discussion, HiSeven shares his deep experience, precisely analyzing these pain points and introducing the practical solutions offered by AINFT’s AI aggregation platform.

First, the issues of “entry difficulty” and “high decision costs”: traditional AI services require users to repeatedly register with email, manage passwords, and even link overseas payment methods—complex processes that can discourage many potential users. AINFT’s AI aggregation platform fundamentally redefines login experience by leveraging Web3 wallets (like TronLink) for “one-click signing,” simplifying access. Meanwhile, the platform integrates multiple mainstream large models in one place, allowing users to switch freely within a unified interface without jumping between websites or apps, greatly reducing hidden costs of model selection and trial-and-error.

This shift is more than just simplifying operations. It essentially frees users from the tedious management of “finding and switching tools,” maintaining workflow continuity and focus. When a creative task shifts from text generation to image creation, or from coding to data analysis, users no longer need to interrupt their flow or switch platforms—they can seamlessly invoke the most suitable AI capabilities within the same scene. This seamless, task-centered experience is a key step in transforming AI from “isolated functions” into a true “productivity pipeline.”

Second, the core pain points of “rigid payment models” and “high payment thresholds”: traditional AI services often use monthly or yearly subscriptions, requiring users to pay upfront for uncertain or low-frequency needs, leading to wasted funds. AINFT’s platform introduces key innovations:

1. “Try before you buy”: New users receive 1 million free points upon registration, allowing ample exploration without immediate payment decisions.

2. “Pay as you go”: Supports on-chain payments with assets like USDT, TRX, and specific NFTs, enabling “pay for what you use.” This approach aligns with high-frequency, fragmented usage habits, eliminating the need for long-term subscriptions and reducing waste. Users who recharge with NFTs can also earn an additional 20% points bonus.

AINFT demonstrates that its core advantage isn’t just pushing the performance limits of a single model but reshaping user experience through product mechanisms combined with Web3 payment capabilities. It aims to free powerful AI capabilities from cumbersome barriers, making them accessible.

This marks an important shift from “model-centered” to “user flow-centered” AI services, truly adapting technology to human habits rather than the other way around. For users, this provides a frictionless starting point: from here, AI ceases to be just a “tool to use” and becomes a natural extension of thinking and productivity interfaces.