Everyone pays attention to Clawdbot, while I only care about whether my AI can truly conduct transactions Author: Bill Sun Introduction: Bill Sun, a Ph.D. in Mathematics/AI from Stanford and a participant in the early discovery of the Google Transformer model, attracted millions of views on X (Twitter) for his tweets over the weekend. He pointed out that the real challenge for AI agents is how to enable agents like Clawdbot to reliably engage in high-value economic activities, such as automated execution of financial transactions. Their company recently launched AIUSD.ai, aiming to build a crucial "Money layer" for AI agents, creating a native money/wallet system based on "machine native" tokenized assets for agents. The article points out that this system completely solves the reliability bottleneck of AI agent execution. It not only effectively avoids the high costs caused by potential user asset key theft and transaction execution errors of agents such as Clawdbot, but also achieves unified operation of all chain/exchange assets by agents, ensuring end-to-end stability and reliability of agent operations. The Agent system is equipped with an AI asset management Agent team and a trader Agent team for each user: continuously monitoring the market and risk control to complete high-frequency trading level execution and economic interaction with other agents (such as trading, transfer, paid learning of new skills). The following is the full text of the article. Now everyone is chasing the hot topic of Clawdbot ->Molty ->Openclaw. Screenshots can be seen everywhere: My inbox was empty while I was sleeping. ”The meeting was automatically scheduled. The research was completed before drinking coffee. It felt like Jarvis had finally appeared. But after developing with Openclaw and Claude Code for a while, I became more aware of one thing: most AI agents today provide emotional value rather than economic benefits. They will think. They will analyze. They will explain. And then they stopped there. Because when funds are truly needed, humanity still faces bottlenecks. The real problem is that no one is willing to admit that Openclaw can tell you: "Market sentiment is changing." "Nvidia's (NVDA) volatility pricing error. ”Tesla's kinetic energy is about to collapse. ”But what will happen next? You may be busy with other things and not have time to open a brokerage account like Charles Schwab to click on 'trade'. When you have time to operate, the Alpha opportunity may have been missed long ago. The trading experience of tokenized assets is more fragmented, as assets are distributed on different chains and you must: Open multiple wallets Find out where your liquidity is Cross chain bridging assets Handling gas fees, slippage, and execution time The bottleneck of manually setting risk control is not intelligence. The problem lies in execution. Artificial intelligence has a brain, but no hands. What changes will occur when we stop asking and start entrusting? I no longer seek advice from AI, but instead start giving it 'intent'. Not asking: What do you think? But rather: just do it like this. For example: "Rotate idle funds to NVDA's exposure. ”If the volatility surges, the risk will be automatically reduced. If TSLA falls below the trend, switch asset allocation. ”This is what impressed me about AIUSD. ∆ AIUSD Agent trading is like hiring a trader who sits in a room, monitoring the market 24 hours a day, waiting for my instructions, and executing trades immediately through intelligent order routing and minimizing trading shocks. Real case: Meta vs Gold, tokenized asset trading executed by agents. Here is a simple but very convincing scenario. We ran an Openclaw Agent driven by Claude Opus 4.5. Its task is to monitor the volatility of NVDA, TSLA, Meta, BTC, gold, and silver driven by profitability. The agent detected on January 29th that Meta had a sustained upward trend after the morning. Gold and silver have higher downward volatility and overall risk due to adjustments in futures margin rules. Considering the on chain liquidity of tokenized assets and the current investment portfolio situation, the agent has decided to reduce its exposure to tokenized gold. Rotate funds to the tokenized stock Meta. Agent uses AIUSD to perform the following operations: aggregate fragmented funds on multiple EVM chains; Automatically reduce PAXGOLD exposure on Ethereum; Convert to a unified funding layer; Establish tokenized Meta exposure on SOLANA; Add automatic trading protection at the execution level with stop loss points for price drops. No need for an app. No need to switch chains. No need to click late at night. The agent did not disturb me. It directly completed the entire process. It should have been obvious why tokenized stocks can change everything after the GameStop event five years ago. The failure in 2021 was not due to individual trading, but rather to infrastructure. The market fluctuates in real-time, but settlement is not like that. Recently, Vlad Tenev, CEO of Robinhood, wrote about this: [Interested readers can click here to view the full text] His conclusion is simple: real-time markets require real-time settlement. This means tokenization. And tokenized stocks have the following characteristics: real-time settlement, 24/7 trading, machine-readable, and can be executed by agents without intermediaries. This is no longer a crypto ideology, it is financial physics. Why are AI agents and tokenization inseparable? The operation mode of AI agents: continuous operation on a global scale without emotions, intolerance for delays, traditional financial operation: limited to market trading time, settlement delays, each step requires manual supervision. These two systems are incompatible with each other. And tokenized assets are financial instruments that possess the following characteristics simultaneously: they can be circulated at machine speed, combined through programming, and fully delegated to agents, which is a key missing link in "agentic finance". AIUSD's vision is not to make a better trading app. We are building a 'Money layer' for AI agents. AIUSD is committed to building a system that meets the following conditions: cross market funds are uniformly managed, execution instructions are precise to ensure the reliability of transactions, Agent risk monitoring system, programmable Agent can perform end-to-end operations, Openclaw proves the thinking ability of AI. Tokenization makes the market 'machine native'. The existence of AIUSD is to connect these two. In the era of AI, Alpha will not belong to the smartest humans, but to those who hand over financial control to machines. Welcome to try AIUSD https://aiusd.ai/