Exclusive Interview with Bitget AI Head Bill: In the Era of AI Trading, How Far Are We from "Easy Earnings"?

Author: Frank, PANews

A "little lobster" has stirred the entire tech circle. The emergence of OpenClaw has excited everyone; on an ordinary personal computer, AI can be granted operational permissions to help you check emails, write code, and even operate trading accounts. The overwhelming online cases describe it in mystical terms: "You won't even need to work anymore." But when it is actually installed, most people find that this is not the case.

In the field of cryptocurrency trading, the temperature difference from frenzy to calm is particularly evident. Over the past two years, almost every exchange has launched its own "AI Agent," but most remain at the stage of chat assistance; you ask it a question, and it writes you a lengthy analysis, and that's it. The appearance of OpenClaw seems to have opened Pandora's box, showing everyone the possibility of AI "doing things" rather than just "talking."

However, this has triggered new challenges. As a leading figure exploring the frontier of AI trading, Dr. Bill, head of Bitget AI, has profound insights on this matter. PANews conducted an in-depth interview with Bill. Before joining Bitget, Bill held senior positions at several leading internet and technology companies, leading the large-scale implementation of multiple core algorithms and AI platforms, and has published dozens of papers at international conferences and holds numerous patents.

Now, fully responsible for Bitget's AI strategic planning and intelligent trading technology development, he is committed to promoting the deep integration of AI and cryptocurrency trading scenarios. Facing the current Agent craze, this leading expert's judgment is extremely calm: "Most ordinary people are not used to being managers; suddenly giving them 10 AI subordinates, how to command, divide work, and assess performance is an art in itself."

Enthusiasm will eventually fade, but capabilities have already been recognized. The real question becomes: who can package this capability into a product that ordinary people can use?

In the conversation with Bill, PANews attempted to dissect the real path from concept to implementation of AI trading from the perspective of a product designer. In Bill's view, Bitget's intensive launch of the Agent Hub and GetClaw AI products is not simply "doing what others do," but rather a natural process of internal product overflow. "In summary, it's about timing, location, and harmony."

Timing refers to how OpenClaw ignited market awareness; location refers to the deep accumulation from the continuous iteration of our AI assistant GetAgent launched last year; and harmony refers to the internal validation of product value, which has led to external openness.

The AI Product Landscape of Bitget: A Three-Tier Structure from GetAgent to GetClaw

To understand Bitget's layout in AI trading, it is essential to clarify the relationship between its three products. From an external perspective, names like GetAgent, Agent Hub, and GetClaw can easily confuse people, but in Bill's explanation, this is actually a clear evolutionary route.

In June 2025, Bitget launched GetAgent within the app, which is an AI trading assistant in the form of a chatbot. According to Bill, GetAgent has undergone multiple iterations: from the initial chat responses, gradually adding one-click ordering, news aggregation, and expanding to all categories of trading such as U.S. stocks, gold, and silver. "Each iteration is driven by user needs, expanding more and more." However, regardless of how it expands, the essence of GetAgent remains "chat-driven"; it can answer questions and provide suggestions but cannot help users independently execute complex trading tasks.

The turning point occurred after the release of OpenClaw. Bill revealed that after OpenClaw was launched, Bitget quickly built its own version internally. "After using it internally, the feedback was very positive, which naturally led to the idea: can we also do a major upgrade of GetAgent?" Following this line of thought, Bitget packaged its internally refined MCP capabilities for external use and officially launched Agent Hub on February 13 of this year.

Agent Hub is aimed at "relatively skilled" professional players.

It provides four layers of capability interfaces, from shallow to deep:

API is the atomic-level interface call, with the highest threshold, requiring programming and key management;

MCP plays the role of a "universal interface," allowing external AI applications to directly read Bitget's data and execute operations;

CLI is aimed at developers, supporting direct calls to all APIs through terminal command lines;

Skills are the core of this upgrade, equivalent to packaged "business modules." Through Skills, the originally rigid API code is transformed into skills that AI can directly call (such as querying rates, analyzing candlesticks, monitoring prices, placing orders), allowing AI to achieve a leap from "intent understanding" to "action execution."

Bill made a very intuitive analogy using a USB drive: "The USB drive itself has storage skills for storing, reading, and writing, but to make it work, it needs a USB interface to connect to devices, which is equivalent to MCP. However, having just the interface is not enough; it also requires the cooperation of storage and various protocols to complete the full interaction. This entire combination constitutes a Skill."

But Agent Hub still has a threshold for ordinary users.

So on March 14, Bitget launched GetClaw, an AI trading assistant based on Telegram, which is ready to use without any installation. Users can access it through a link, log in to their accounts, and use it; the platform bears the cost of calling the large model, and users are completely unaware of it. Bill summarized it in one sentence: "Ordinary users are recommended to use GetClaw; it's a tool that is already assembled and can be played with immediately; professional players are recommended to use Agent Hub, selecting suitable Skills, like building with Legos, to create their own castle."

These three products form a clear progressive relationship: GetAgent refined the underlying MCP capabilities, which were then opened up in Agent Hub, and these capabilities were embedded into GetClaw, lowering the usage threshold to the minimum. From chatbot to developer tool to one-click product, Bitget's AI product line covers the entire user spectrum from geeks to novices.

"Just say a word to monitor the market," what AI trading really changes

The product architecture is just the skeleton; what truly excites users is the experiential transformation brought by AI in specific scenarios. In discussions with Bill, a recurring keyword is "threshold."

The traditional trading process is a long chain: obtaining information, analyzing decisions, executing orders, monitoring the market, and reviewing summaries, with each link relying on manual operations. If users want to do conditional trading or quantitative strategies, they either have to write programs to adjust APIs or configure a bunch of complex parameters on the platform.

In Bill's view, this is precisely where AI's most valuable entry point lies: "These functions can be achieved without Skills or GetClaw; you can just write a program. But the problem is, writing programs is simple for programmers but has too high a threshold for ordinary users. What we are doing today is allowing users to achieve the same effect by just saying a word."

He gave a specific example: a user says, "When Bitcoin drops by 3% within a minute, help me increase my position by 50%," and the system will automatically convert this into a timed task, which actually needs to accomplish three things:

1. Real-time monitoring of Bitcoin prices

2. Calculating the price difference every minute

3. Immediately executing the position increase operation once the conditions are met

This logic, which could only be achieved by programmers in the past, can now be completed by anyone with just a sentence.

Less than 40 hours after GetClaw went live, market monitoring reminders became the most explosive use case. This is not surprising; on traditional platforms, configuring market monitoring alerts requires users to understand various indicator parameters, "spending half a day and still not necessarily succeeding." Now, even for complex monitoring logic involving multiple indicators like MACD and CCI, users can describe their needs in natural language, and the system can help them achieve it.

However, Bill believes that the true transformation of AI monitoring is not just about "being able to do it," but also about "being able to optimize it." "On traditional platforms, if the configuration is not done well, users would give up, but now you can tell it, 'You're wrong, reflect on how to improve,' and keep adjusting until satisfied." This kind of continuously iterative interaction is a huge satisfaction for the vast long-tail user base.

In the traditional stock market, the proportion of quantitative trading is increasing, and in the relatively mature U.S. market, it can even exceed 70%. Ordinary retail investors face microsecond-level competition from institutional opponents, with almost no chance of winning. Bill summarizes the significance of AI trading as a form of "equalization": "Bitget's vision in the AI field is to enable 100 million users to stand shoulder to shoulder with Wall Street, in other words, to allow them to achieve the operational logic and execution capabilities of top traders. In the past, it was something they thought of but couldn't do; today, as long as they can think of it, they can do it."

Four Locks of Trust: The Safety Boundaries of AI Operations with Real Money

When AI moves from "giving advice" to "executing for you," the power of functionality is not the biggest challenge; trust is. In Bill's view, this point cannot be overstated: "The biggest concern for ordinary users is 'Is it safe to use it?' This trust must be established well. Once there are one or two security issues, no one will use it."

Around this core concern, Bitget has designed a four-layer isolation system.

• The first layer is identity isolation, accurately identifying user identity in each conversation.

• The second layer is memory isolation, completely isolating and confusing the conversation memories between different users to ensure personal privacy is not leaked.

• The third layer is permission control, determining what data and tools can be called based on roles.

• The fourth layer is credential and fund isolation, where API keys are limited to triggering use, and transactions are executed in a sub-account sandbox.

The sub-account sandbox mechanism is a very pragmatic design. Bill gives an example: "For instance, if the main account has $1,000, the user can only transfer $50 to the sub-account for AI operations, making the risk much more controllable." This means that even if the AI makes a judgment error, the risk exposure is strictly controlled within the user's preset range.

This safety-first approach is also reflected in Bitget's attitude towards the Skills market. Currently, all Skills are developed and maintained by the official team and are not open to third parties. Bill's explanation is very straightforward: "If we open the Skill Market to allow more people to participate in building it, security issues will inevitably arise. For example, if a hacker says, 'I'll put one in for you,' and users incur financial losses, that would be inappropriate. We prefer to have none than to risk losing everything. After all, in the asset market, earning quickly is not as good as surviving long."

The caution demonstrated by OpenClaw's past experience proves the rationality of this approach. It operates on personal computers in an almost unrestricted manner, which, while exciting, has also spawned an absurd new industry, "helping you cleanly uninstall the lobster" has itself become a profitable business.

At the level of large model calls, Bitget initially chose to have the platform bear the costs rather than letting users configure tokens themselves. On one hand, this is for safety considerations, and on the other hand, it is for technical reasons: "Our Skills and MCP are deeply optimized for various built-in large models; if users switch to other models at will, the effectiveness will be greatly reduced." Currently, the platform provides each user with a daily free calling quota of $10, and the pricing model will be adjusted based on market feedback.

80% of Tasks Can Be Done, but 20% of Decisions Still Depend on Humans

When discussing the realistic boundaries of AI trading capabilities, Bill candidly admits that the reality is not optimistic: "There are people online giving AI $100 to try to make it $1,000, but they find that this rough operation has a very high probability of losing everything."

The capabilities of AI trading today cannot guarantee that users will make money. Bill summarizes the current reality with the "80/20 principle": in a complete trading process (which may involve 100 tasks), AI can efficiently complete 80 of the complex tasks, such as information organization, real-time monitoring, conditional execution, and data review. However, the 20 core decisions that truly determine profit and loss are still beyond AI's reach.

Last year, Bitget held a playful AI trader competition to test the boundaries of AI capabilities, which provided a vivid footnote: many AI strategies ultimately ended in losses. The reason is not complicated; AI lacks emotions, which sounds like an advantage, but also means it cannot respond to extreme events like "sudden wars." Bill mentioned that when AI was extensively used to execute trades in the U.S. stock market, there were also instances of abnormal phenomena like flash crashes and spikes.

"Today, it plays more of a high-level assistive role, much like the transition from autonomous driving L1 to L5." Bill uses this analogy to position the current stage of AI trading development. From a trend perspective, AI's capabilities are indeed overcoming remaining challenges one by one, but when it comes to long-term creativity and empathetic judgment in extreme situations, machines still have significant bottlenecks.

However, Bill also provided a relatively optimistic judgment: "The technical closed loop around fully automated trading may be basically realized next year, but that does not mean it can guarantee continuous profitability." In other words, there is still a considerable distance between "being able to run" and "being able to earn."

From Trading Tools to "AI Account Operating System": Bitget's Ultimate Vision

Since AI cannot completely replace human traders in the short term, what is the endpoint of Bitget's AI strategy? Bill provided answers from three dimensions.

The first dimension is "panoramic trading," which also echoes Bitget's previously proposed UEX (Universal Exchange) strategy. Not just cryptocurrencies, with the advancement of asset tokenization, traditional financial categories such as gold, silver, and U.S. stocks are also being integrated. Bitget hopes to use AI to help users complete trading operations across all categories on one platform, "enabling users to have the comprehensive trading capabilities of Wall Street traders."

The second dimension is the expansion of a global ecosystem. Combining the capabilities of Bitget Wallet, AI will be introduced into Web3 payments and global business scenarios to lower the operational threshold for cross-border trading and payments.

The third dimension, which Bill describes most vividly, is to create a "long-term account operating system" based on Bitget. The core of this concept is to establish a "high-trust fund execution layer," where multiple Agents will collaborate to help users with various tasks, supported by a cross-end, cross-scenario "long-term memory system."

In Bill's description, this memory system will analyze and integrate users' past trading habits, historical operations, and even small behaviors in the app to form a deep personal profile. "Ensuring that users' trading logic remains consistent across different platforms and scenarios, rather than a fragmented experience." This ability for continuous learning and adaptation is fundamentally different from one-time tools.

He used a very everyday analogy to explain this gradual trust process: "Just like initially buying a home cleaning robot only to have it vacuum, after using it for a long time and building trust, you are willing to let it take on more tasks." AI needs to first prove itself reliable in small matters, then gradually gain greater permissions and trust, with the ultimate goal of "growing with you and accompanying your asset appreciation."

From GetAgent to Agent Hub to GetClaw, Bitget's AI products have completed the leap from chatbot to task execution layer in less than a year. The intensive layout of major exchanges also indicates that AI trading is no longer an optional direction but a fundamental capability for future competition.

However, from the current reality, AI is better at replacing the "manual labor" in trading rather than the "intellectual labor." 80% of the complex tasks can be handed over to machines, but the 20% core judgments that determine profit and loss still require human input. Technology can lower the threshold for trading but cannot completely eliminate trading risks.

AI has given everyone access to Wall Street's toolbox, but the toolbox contains both opportunities and respect.