Final ultimatum! The financial industry's "shrimp farming" craze suddenly hit by regulatory triple blow—is the institutional narrative for $BTC and $ETH about to change?

The road to transformation has never been smooth. Recently, regarding the open-source intelligent agent OpenClaw, three security alerts have appeared within a week. One of these warnings, targeting a single software, is quite rare and fully exposes the core contradiction faced by the financial industry in the AI wave: a desire for efficiency paired with a fear of losing control.

Market observers have found through research that banks, insurance companies, and securities firms are each heading down three very different evolutionary paths.

The banking sector’s attitude is the most restrained. Several state-owned banks have explicitly banned employees from installing OpenClaw privately and are instead opting for in-house development of proprietary intelligent agents. However, these efforts are currently limited to peripheral office systems like email processing and meeting minutes, far from touching core functions such as intraday trading and fund clearing.

High deployment costs are the primary obstacle. The initial cost for private deployment of such intelligent agents ranges from 3 million to 5 million RMB, supporting only about a hundred employees. To access more powerful cloud models, users face costly token consumption cycles. Some compare current token usage to 2009’s 30MB monthly 2G data plans—expensive and not very durable.

Deeper constraints stem from outdated hardware and weak data foundations. Many financial institutions’ office computers run old browsers that cannot support modern APIs required by intelligent agents. Their data architecture was not designed for AI; key business semantics are scattered across code or in the minds of veteran staff, making it difficult for AI to understand. This results in an absurd scene: the industry chasing cutting-edge AI in 2026 while still relying on hardware from 2016.

Under multiple pressures, the Matthew effect in the industry is intensifying. Leading banks can experiment within secure sandboxes thanks to their substantial budgets, while small and medium-sized banks with tight IT budgets are likely to be completely shut out.

The insurance industry shows more flexibility. Some top companies have attempted to integrate OpenClaw with office systems and opened internal testing to thousands of employees, but this soon drew regulatory scrutiny. Industry consensus is gradually shifting toward “micro-innovations” in “non-core areas.”

The greatest potential lies in empowering frontline agents. OpenClaw can automatically track clients, build profiles, and handle trivial tasks, freeing agents from information sorting to focus on emotional value and closing deals, greatly boosting individual productivity.

But this is a double-edged sword. Agents handle highly sensitive personal data such as health disclosures and family finances, facing unprecedented exposure risks. Since most business activities are personal, these risks are difficult to prevent and regulate. Efficiency and privacy are colliding head-on in agents’ smartphones.

Securities firms remain in a “wait-and-see” stance. CITIC Securities, GF Securities, and others have banned employees from installing related applications on work computers. Some companies, while not explicitly forbidding it, are cautious and observing.

Research departments are seen as the most promising testing ground. AI can process vast amounts of information, freeing analysts’ productivity. However, for industry activities relying on on-site verification and due diligence, there is a physical gap. More critically, the financial industry’s near-zero tolerance for errors, combined with current general AI’s hallucination issues and lack of traceability, makes it an unacceptable risk for licensed institutions.

Wall Street’s practices offer a reference. An AI tool called Rogo, which connects to core financial database APIs and ensures every conclusion is traceable and cited, has been adopted by institutions like JPMorgan Chase and Nomura Securities. Essentially, it turns AI’s “black box” into a “glass box.”

Finally, all enthusiasm must face a sobering economic reality. The securities industry is currently in a “cost reduction and efficiency increase” cycle, with even Wind terminal procurement being scaled back. In this environment, requesting additional budgets for uncertain token consumption faces huge resistance. Some analysts joke that every “thank you” to AI burns real money.

Every wave of technological change impacting finance follows a remarkably similar script: fear, blockade, internal imitation, and ultimately full adoption. From internet payments to blockchain, this pattern persists. The current AI wave is in the transition from “blockade” to “internal imitation.”

The industry is waiting for a domestically developed optimal solution that perfectly balances security, cost, and business features. This answer will not fall from the sky; it is quietly growing within bank security sandboxes, insurance agents’ workflows, and analysts’ meticulous calculations of each token. For those following assets like $BTC and $ETH, this “silent revolution” happening behind the scenes of traditional finance may just be beginning to show its profound impact.