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

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

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

Based on this, a discussion focused on “how ordinary users can truly make good use of AI” has emerged. This X Space roundtable, jointly hosted by Sun Wukong Ecosystem and AINFT, starts from the perspective of ordinary users and invites industry KOLs to explore: why AI has once again become a main theme amid market turbulence, and how AINFT, through 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 dialogue.

From Capital Narratives to Value Applications: Why Can AI Forge a Main Trend in a Volatile Market?

In the context of recent market sentiment turning cautious and capital pressure mounting, the AI sector’s attention has not waned but increased. Several guests analyze the core logic behind this phenomenon from different angles, generally believing it’s driven not by simple market hype but by deeper structural changes.

First, regarding market attention, AI’s “certainty” has replaced “imagination space.” Multiple guests point out that the current market environment is experiencing “truth from falsehood.” Mr. Mi Si believes that projects relying solely on narratives are unsustainable, while AI demonstrates practical empowerment for enterprises to reduce costs and improve efficiency, making it a value anchor that can withstand 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, making market choices increasingly realistic. Capital naturally flows toward fields capable of “self-sustaining growth.”

Crypto analyst Peter and Mo Yu supplement from a capital logic perspective, believing 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 strengthen market confidence in long-term allocation to AI tracks.

Focusing on the track itself, guests point out three essential 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 that needs to be opened separately but is embedded like water and electricity into various software and processes.

From “model competition” to “ecosystem integration”: Mr. Mi Si notes that the industry is moving toward infrastructure and protocol development, with standards like MCP (Model Context Protocol) enabling AI models to be assembled with Web3 tools 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 assets” 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 in AI writing, programming, design, and financial analysis make it a measurable productivity partner with clear investment returns.

Overall, the revival of AI this round hinges on its crossing from “concept narrative” to “application landing.” It is not only a promising investment track but also a real tool reshaping workflows and business models. This “application certainty” constitutes its unique and solid appeal amid 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 deep personal experience, precisely analyzing these pain points and presenting practical solutions offered by the AINFT 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 bind overseas payments—procedures that are cumbersome and likely to discourage potential users. AINFT’s platform fundamentally redefines login experience by integrating Web3 wallets (like TronLink) for “one-click signing,” simplifying the process. Meanwhile, the platform consolidates multiple mainstream large models into one interface, allowing users to switch freely without jumping between websites or apps, reducing hidden costs of model selection and trial-and-error.

This shift is more than just operational convenience. It essentially frees users from the tedious management of “finding and switching tools,” allowing their workflows to remain continuous and focused. When a creative task switches from text generation to image creation, or from coding to data analysis, users don’t need to interrupt their thinking 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 “inflexible payment models” and “high payment thresholds”: traditional AI services mostly adopt monthly or yearly subscriptions, requiring users to pay fixed fees upfront for uncertain or low-frequency needs, leading to idle funds and waste. AINFT’s platform introduces key innovations:

1. “Try before you buy”: new users receive 1 million free points upon registration, enough to explore various features without immediate payment decisions.

2. “Pay as you go”: supports multiple on-chain assets (like USDT, TRX, and specific NFTs) for micro-payments, truly enabling “pay for what you use.” This approach aligns with high-frequency, scattered usage habits of AI tools, eliminating the need for long-term subscriptions and waste. Users who recharge with NFTs can also earn an additional 20% points reward.

AINFT’s practice shows that its core advantage is not solely in pushing the performance limits of individual models but in combining product mechanisms with Web3 payment capabilities to reshape user experience flows. It aims to free powerful AI capabilities from cumbersome constraints, making them accessible.

This marks an important shift in AI services from “model-centered” to “user flow-centered,” truly adapting technology to human habits rather than the other way around. For users, this provides a frictionless starting point: from here, AI will no longer be just a tool to “use,” but a natural extension of thinking and productivity interfaces.