#Gate广场AI测评官

Gate AI Strategic Layout Analysis (Compared with bn)

This is the final article in our review of Gate AI products. Let’s take a look at Gate Exchange’s AI strategic layout and compare it with bn.

1. Strategic Goal and Positioning Differences

Gate: Building a “Unified Entry Point for Intelligent Web3”

Through Gate for AI, integrating five core capabilities: CEX trading, DEX liquidity, wallet management, on-chain data, and information analysis, forming a closed-loop ecosystem covering the entire “Analysis - Decision - Execution” process. Its core is to reduce AI call complexity via standardized interfaces (MCP protocol), promoting AI from an auxiliary tool to a foundational trading infrastructure.

bn: Enhancing execution efficiency and developer ecosystem

Centered around Ai Pro, focusing on automated trading execution and high-frequency strategy optimization, isolating risk with independent AI accounts, and opening APIs to support developers in building complex strategies. The target users are more inclined toward professional quantitative teams, emphasizing low latency and high liquidity advantages.

2. User Coverage and Experience Layering

Gate: Lowering barriers for retail participation

Zero-code strategy generation: Create executable strategies using natural language descriptions

Visual operation interface: Skills Hub modular design to accommodate non-technical users

Full terminal coverage: Unified AI service entry point via Web/App

bn: Serving professional traders

High-frequency trading optimization: Order latency of 0.02 seconds (perpetual contract scenarios)

Developer toolchain: Providing SDKs, backtesting sandbox, and on-chain data APIs

Institutional-grade risk control: Real-time monitoring of millions of transactions with AI anti-fraud systems

3. Ecosystem Collaboration and Industry Impact

Gate’s “MCP + Skills” dual protocol

Standardizing cross-platform capability calls via MCP protocol, with Skills Hub aggregating third-party strategies (e.g., open-source solutions on GitHub)

Typical case: Users can call DEX liquidity data within Gate for AI to directly trigger CEX hedging trades

bn’s on-chain data barrier

Using on-chain transaction data to train proprietary prediction models, strengthening Alpha capture capabilities

However, it does not open DEX/cross-chain data interfaces, making its ecosystem openness weaker than Gate.

4. Challenges and Limitations

Gate: Needs to prove the long-term effectiveness of zero-code strategies (backtesting ≠ live trading), and the third-party Skills security review mechanism is not publicly disclosed.

bn: High automation leading to operational risks (e.g., short-term liquidations caused by algorithm errors in 2025), and a steep learning curve for retail users.

Summary: Gate focuses on ecosystem integration and democratized AI to expand its user base by lowering barriers; bn concentrates on execution efficiency and data monopoly, consolidating institutional and high-frequency trading markets. Both define the competitive paradigm of AI trading infrastructure from “breadth” and “depth,” respectively.