Space Recap | AINFT Builds Foundational Infrastructure for the AI Agent Era—Saying Goodbye to Expensive APIs and Model Barriers

Today, AI Agents are evolving from mere “efficiency-enhancing tools” into “value-creation entities,” sparking a nationwide frenzy dubbed “shrimp farming”—a metaphor for cultivating AI Agents. Yet beneath the surface clamor flows a strong undercurrent of challenges: prohibitively high API call costs, fragmented ecosystems among top-tier models, and widespread neglect of critical aspects like data feeding and strategy optimization—issues that leave many users’ expectations severely unmet in real-world deployment.

Facing this pervasive “AI anxiety” sweeping across the internet, this edition of Space cuts through the fog to re-examine the commercial monetization logic of AI Agents and explore how individuals can build robust moats by cultivating their “core competencies.” Furthermore, from an industry-ecosystem perspective, this article unpacks AINFT—a foundational AI infrastructure dedicated to lowering user adoption barriers—and offers a comprehensive analysis of how market participants can genuinely advance toward AI-driven value creation—beyond the narrative bubble.

Confronting AI Anxiety: Building Real Moats in the AI Agent Era Through Personal “Core Competencies”

Amidst the rapid evolution of AI technologies, a form of “fear of missing out” (FOMO)-driven AI anxiety is spreading across the internet. Fearing obsolescence, ordinary users are rushing headlong into the “shrimp farming” (i.e., building and training AI Agents) race, scrambling to seize the next big opportunity. Yet beneath the surface euphoria lies a pressing question: Can average users truly profit from AI? Is this a genuine wealth opportunity—or merely another narrative bubble? Our guests engaged in a deep discussion on this contentious issue.

Niu Moguang (a pseudonym) pinpointed the core contradiction fueling today’s “shrimp farming” craze: while Agents possess 24/7 operational capability, most so-called “shrimp farmers” in the market are actually paying premium fees for API calls or expensive courses—effectively working as “paid laborers.” Moreover, the time cost of repeated fine-tuning and uncertainty caused by shifting platform rules mean ROI (return on investment) for average users is likely negative.

Crypto.0824 illustrated with an example: someone pays ¥99 for a one-month AI Agent that automatically posts videos—but without effective distribution or user-acquisition strategies, the content generated inevitably suffers from abysmal traffic. In such transactions, users pay not for tangible outcomes but for an illusory “sense of participation.” He emphasized that while AI Agents represent a true wave of the times, blindly jumping on the bandwagon without understanding concrete business logic only means passively paying tuition fees for the hype.

So—can you really make money with AI Agents? Our guests believe opportunities remain—but only for those who are prepared. Whether AI Agents generate income hinges entirely on how users position them: as speculative assets to ride the trend, or as practical tools capable of optimizing their own businesses and solving real pain points. Only the latter holds genuine potential to cross the profitability threshold.

In summary, AI Agents are amplifiers of efficiency—not magic money-printing machines. Those who truly profit from AI are individuals holding “core assets”: exclusive data, proven trading strategies, powerful content-distribution channels, or well-defined business logic. Without these authentic capabilities at the core, attempting arbitrage using generic, low-barrier tools will inevitably render users mere sources of profit for others.The best way to overcome AI anxiety is first to clarify your own core strengths—and then let the AI Agent become your digital employee.

In this process, the entire industry must also reassess the role of the “shovel sellers.” In a healthy industrial ecosystem,“shovel sellers” should act as genuine enablers—road-builders and infrastructure providers for users. When users possess unique strategies and clear intent, and when truly high-quality infrastructure drastically lowers trial-and-error costs and seamlessly connects underlying execution layers, it won’t just be key to bursting the narrative bubble—it will be the essential path forward for guiding society past anxiety and into genuine AI-driven value creation.

Bursting the Narrative Bubble: Building Foundational Financial Infrastructure for AI Agents

Leveraging a product logic that directly addresses industry pain points, AINFT—now a pivotal component of the TRON ecosystem—has seen strong growth momentum.Its current user base has surged past the 600,000 mark. Addressing the previously mentioned issues of exorbitant API costs and development friction from repetitive tuning,AINFT proposes an infrastructure solution designed for broad user accessibility—offering “one-stop multi-model service,” coupled with a “no-subscription, pay-as-you-go” model and 1 million free credits—to effectively alleviate financial pressure and frictional costs associated with large-model invocation, thereby providing highly elastic foundational support for rapid Agent iteration and deployment.

Specifically, AINFT delivers the following core capabilities to tackle industry pain points:

• Unified API Key Access to Global Top-Tier Compute Resources: AINFT fully supports the most cutting-edge large language models currently available—including OpenAI’s GPT-5 series, Anthropic’s Claude 4.5/4.6 family, and Google’s Gemini 3 series. It has recently added MiniMax-M2.5, Kimi-K2.5, and GLM-5—several leading models. Users no longer need to juggle multiple platforms: with just one unified API Key, they can seamlessly switch between and invoke all these top-tier models. This eliminates the high overhead of managing multiple accounts and significantly shortens both cross-model development cycles and application time-to-market.
• Web3-Native Payments and Transparent, Usage-Based Billing: The platform completely dismantles traditional monthly subscription barriers. Developers worldwide can log in and top up assets via mainstream Web3 wallets—including MetaMask, TronLink, Binance Wallet, and OKX Wallet. Currently, the platform accepts USDT, USDC, TRX, and BNB, among other major crypto assets. Developers choosing to top up using NFTs receive an additional 20% bonus credit. Crucially, the platform strictly implements a “top-up-and-use, pay-per-usage” model—charging precisely based on tokens consumed—effectively preventing resource idleness and capital waste, ensuring every cent invested maps directly to actual output.
• One Million Free Credits to Break Down Trial-and-Error Barriers: To address early-stage capital consumption pain points for ordinary users entering “shrimp farming,” the platform grants new users 1 million free credits. This initiative effectively creates a generous sandbox environment for experimentation—dramatically lowering initial entry barriers.

In conclusion, as foundational infrastructure serving AI Agent builders, AINFT follows a logic grounded in objectively lowering technical barriers while amplifying the value of core strategies. The tool itself makes no promise of creating wealth out of thin air—but for users possessing clear commercial intent, this system enables them—through minimal trial-and-error costs and highly efficient large-model orchestration—to refine generic AI tools into bespoke digital employees delivering tangible business value.