Space Review | Bidding farewell to high API costs and model barriers, AINFT builds the underlying infrastructure for the AI Agent era

Currently, AI Agents are evolving from mere "efficiency tools" to "value-creating entities," sparking a nationwide craze for "shrimp farming" (developing AI Agents). However, beneath the noise, there are undercurrents. The persistently high costs of API calls, the ecological barriers that make it difficult to connect major top models, and the neglect of "data feeding" and strategy optimization for AI Agents due to blind following have significantly dampened many users' expectations in practical implementation.

In the face of the widespread "AI anxiety" across the internet, this issue of Space will clear the fog, re-examine the commercial monetization logic of AI Agents, and explore how the public can build a solid moat with "core strengths." Additionally, this article will interpret the underlying AI infrastructure AINFT, which aims to lower user entry barriers from an industry ecosystem perspective, providing a comprehensive analysis of how market participants can truly embark on the path of value creation with AI after crossing the narrative bubble.

Confronting AI Anxiety: Establishing a True Moat in the Era of AI Agents by Solidifying Personal "Core Strengths"

In the wave of rapidly evolving AI technology, a form of AI anxiety known as "fear of missing out" is spreading across the internet. Ordinary people, fearing being left behind by the times, are flocking to the "shrimp farming" (developing AI Agents) track, trying to seize new opportunities. However, beneath the fervent surface, the question remains: can ordinary users truly make money with AI? Is this a new round of wealth opportunities or just another narrative bubble? The guests engaged in an in-depth discussion on this controversy.

Niu Mo Wang first pointed out the core contradiction in the current "shrimp farming" craze. He noted that while Agents have the capability to work 24/7, most so-called "shrimp farmers" in the market are actually paying for expensive API call fees or paid courses, effectively in a "paying to work" state. Furthermore, the time cost of repeated debugging and the uncertainty brought about by changes in platform rules make it very likely that ordinary users' ROI (return on investment) is negative.

Crypto.0824 provided an example, stating that someone spent 99 yuan to purchase a month of an automatic video posting AI Agent, but without effective distribution and customer acquisition strategies, the content produced often receives little traffic. In this transaction, what the user bought was merely an illusory sense of "participation." He emphasized that while AI Agents are indeed at the forefront of the times, blindly following trends without understanding the specific business logic often results in merely paying tuition for this craze.

So, can AI Agents really not make money? The guests believe that opportunities still exist, but they are only available to those who are prepared. Whether one can make money with AI Agents depends on how users position them: as an investment chip to follow trends or as a genuine tool to optimize their business and solve pain points. Only the latter has the potential to reach the threshold of profitability.

In summary, AI Agents are merely amplifiers of efficiency, not machines that print money out of thin air. Those who can truly make money with AI are those who hold "core assets" in their hands, possessing exclusive data, mature trading strategies, strong content distribution channels, or clear business logic. Without these real capabilities as a core, attempting to profit from generic tools with no barriers will inevitably lead to becoming a source of profit for others. The best way to escape AI anxiety is to first clarify one's core advantages and then let AI Agents become your digital employees.

In this process, the entire industry also needs to re-examine the role of "shovel sellers." In a well-developed industrial ecosystem, "shovel sellers should be the true enablers of users, acting as 'road builders' and 'infrastructure providers.' When users possess unique strategies and intentions, and truly high-quality infrastructure can significantly reduce trial-and-error costs and connect the underlying execution links, this is not only key to breaking the narrative bubble but also the necessary path to lead everyone out of anxiety and towards AI value creation.

Crossing the Narrative Bubble: Building the Underlying Financial Infrastructure for AI Agents

With a product logic that directly addresses industry pain points, AINFT, an important part of the TRON ecosystem, has seen strong growth momentum, with the platform's user count surpassing 600,000. In response to the previously mentioned high API costs and the friction of repeated debugging, AINFT has proposed an infrastructure solution aimed at a broad user base, which not only achieves "one-stop service for multiple models" but also effectively alleviates the financial pressure and friction costs during the large model calling process through a "no subscription, pay-as-you-go" model and 1 million free points, providing highly flexible underlying support for the rapid iteration and implementation of agents.

Specifically, AINFT provides the following core support for industry pain points:

• Unified API Key to Access Global Top Computing Power: AINFT fully supports the most cutting-edge large language models currently on the market, including OpenAI's GPT-5 series, Anthropic's Claude 4.5/4.6 family, and Google's Gemini 3 series. Recently, it has also added several leading large models such as MiniMax-M2.5, Kimi-K2.5, and GLM-5. Users do not need to switch platforms; they only need to obtain a unified API Key to seamlessly switch and call these top models. This not only eliminates the high costs of maintaining multiple accounts but also significantly shortens the development process and application launch cycle for cross-model calls.

• Web3 Native Payments and Transparent Pay-as-You-Go Billing: The platform has completely broken the traditional monthly subscription barrier. Global developers can log in and recharge assets through mainstream Web3 wallets such as MetaMask, TronLink, Binance Wallet, and OKX Wallet. Currently, the platform supports various mainstream crypto assets for recharge, including USDT, USDC, TRX, and BNB. If developers choose to use NFTs for recharge, they can receive an additional 20% bonus in points. At the same time, the platform strictly implements a "pay-as-you-go" mechanism, charging only for the actual Token consumed, effectively avoiding resource idleness and financial waste, ensuring that every investment is linked to actual output.

• One Million Free Points to Break Down Trial-and-Error Barriers: To address the financial consumption pain points faced by ordinary users in the early stages of "shrimp farming," the platform offers new users 1 million free points. This initiative essentially provides the market with a generous trial-and-error environment, significantly lowering the initial entry barrier.

In summary, AINFT, as the underlying infrastructure serving AI Agent builders, follows the logic of objectively lowering technical barriers and amplifying the value of core strategies. The tool itself does not promise to create wealth out of thin air, but as long as users have clear business intentions, this system can assist them in refining ordinary AI tools into exclusive digital employees with real business value, all at a very low trial-and-error cost and with efficient large model scheduling capabilities.