This week, the on-chain data analysis platform Nansen launched an AI-driven integrated trading feature simultaneously on both web and mobile platforms. This new functionality adds direct order placement and cross-chain asset scheduling capabilities to its existing data viewing and on-chain analysis tools, attracting market attention. The core of this upgrade is an attempt to connect data insights, trading decisions, and execution paths through a conversational AI interface, consolidating processes that were previously scattered across various tools such as browser wallets, DEX frontends, and cross-chain bridges into Nansen's own product. Currently, this AI trading feature only supports Solana and Base networks and is clearly in the Pro user testing phase, with functionality boundaries and user scope intentionally tightened. In this experimental scenario, Nansen aims to reshape the interaction between users and DEXs using large-scale labeled wallet data and AI interaction capabilities, while also opening a second front for data platforms to enter trading business and reconstruct business models.

Upgrade of the Entry Point for Trading and Data Convergence

● Product Positioning Evolution: Nansen started as an on-chain data analysis platform, providing market interpretation and address monitoring through a labeling system, fund flow, and address profiling. Now, it has added trading execution and cross-chain capabilities, evolving from a "view-only" data analysis tool to a one-stop entry that integrates on-chain insights and trading operations.
● Trading Aggregation Integration: In terms of specific execution paths, the new feature integrates Jupiter on Solana and OKX DEX on Base as the main DEX aggregators, responsible for finding optimal quotes and liquidity within their respective ecosystems. This allows Nansen to maintain a unified interaction interface on the front end while delegating depth and routing choices to mature aggregators.
● Cross-Chain Routing Collaboration: Cross-chain asset migration is handled by LIFI, providing routing services. Users can complete asset transfers and subsequent trades from one chain to another within the same AI dialogue interface, without needing to switch to an independent cross-chain bridge frontend. The entire process is encapsulated within Nansen's unified experience.
● Non-Custodial Wallet Architecture: To avoid custodial compliance and fund security responsibilities, Nansen adopts a non-custodial wallet solution supported by Privy, ensuring that user assets remain under their own wallet architecture. Nansen primarily acts as an intermediary for signature coordination and instruction issuance, rather than a fund custodian.
● Data Asset Differentiation: Based on the officially disclosed dataset covering over 300 million labeled wallet addresses, Nansen has distilled years of accumulated labeling systems and behavioral data into an AI decision-making foundation. Under the premise of connecting to DEX aggregators, it provides a differentiated underlying asset for AI trading recommendations and asset screening through richer wallet profiles and fund flow samples.

Conversational Order Placement Reshaping Interaction Experience

Traditional on-chain trading processes typically require users to switch between multiple tabs in their browsers, first conducting research on data platforms or social media, then opening a browser wallet to connect to a specific DEX frontend, manually selecting trading pairs, slippage, routing, and fee networks. Cross-chain transactions require opening a separate cross-chain bridge interface, making the entire process unfriendly for newcomers, with high operational costs and cognitive burdens. The AI conversational trading launched by Nansen connects these multiple steps through natural language interaction within a unified interface, allowing users to start with "What asset do you want to buy on which chain, and how much do you want to invest?" The AI is responsible for calling upon the underlying DEX aggregators and cross-chain routing services, providing executable trading plans and paths. Leveraging a large sample of on-chain data covering over 300 million labeled wallets, the AI can consider more dimensions of information when generating trading suggestions, such as fund flow, position structure, and address behavior patterns, theoretically providing users with richer decision-making references than just price and liquidity dimensions. However, this model also has clear boundaries; on one hand, on-chain data itself has latency and noise, and AI recommendations made during high volatility may not timely reflect the latest risks. On the other hand, the "black box" nature of the model makes it difficult for users to fully understand the underlying weights and causal chains, potentially amplifying decision errors and execution risks for high-frequency or large-amount traders. The design of encapsulating cross-chain asset transfers and aggregation routing within the same dialogue interface significantly lowers the cognitive threshold for retail investors accustomed to single-chain operations, allowing users to complete fund migration and trading without needing to understand the details of each bridge and route. However, while convenience is enhanced, it also increases reliance on system stability and AI prompt accuracy; if there are deviations in path estimation or fee assessment, the costs will ultimately be borne by the end users.

Thresholds and Layering During Pro Testing Phase

Currently, this AI trading feature is clearly in the Pro user testing phase, meaning that only paid or recognized professional user groups can experience the complete closed-loop functionality first. Ordinary free users remain at the level of traditional data viewing and some basic functions. This design, which divides experience levels based on payment tiers and professional degrees, allows Nansen to observe real trading behaviors in a more controlled environment, iterating risk control rules and identifying potential security vulnerabilities within a smaller sample, thereby reducing systemic risks that may arise after large-scale opening. On the other hand, it also creates a clear psychological gap within the community. According to public reports and social media discussions, some free users have expressed dissatisfaction with the permission restrictions, believing that the core functionality of AI trading, which represents the future direction of the product, is locked behind the Pro threshold, exacerbating "information and tool inequality" within the platform. From Nansen's perspective, initially targeting high-value professional users for trial and error is beneficial for balancing risk control, compliance, and product feedback quality: professional users are better at identifying early product instability and are more motivated to provide structured improvement suggestions, allowing the team to refine features at a faster pace. However, this "professional user first" strategy creates tension with the crypto industry's emphasis on openness and community narrative—when the AI trading entry that has the potential to change interaction paradigms is only open to a small group, Nansen needs to find a new narrative balance between "serving high-value customers" and "maintaining community inclusivity" to avoid forming a long-term impression of "tool barriers reinforcing centralized power."

Collaboration and Game Theory in the Integrated Entry Competition

Compared to traditional DEX frontends, pure aggregators, or smart wallets, Nansen's entry position and capability boundaries in the trading business are different. The former often starts from "execution," focusing on routing, fees, and asset support ranges, while Nansen begins with "data and insights," extending to execution, naturally bundling research and order placement within the same product. After integrating third-party services like Jupiter, OKX DEX, and LIFI, Nansen contributes user entry points and additional trading volume to these partners, forming a complementarity of traffic and execution capabilities. On the other hand, when data, routing, and execution are all encapsulated within Nansen's AI dialogue interface, the end user's perception of which specific DEX or bridge completes the transaction is weakened, potentially creating new game theory space in terms of brand visibility and bargaining power in the long run. As data analysis, routing selection, and trading execution are further integrated under a unified entry point, the logic of user retention is also rewritten—users no longer come just to "view data," but stay within a complete closed loop of "viewing + thinking + doing," reserving imaginative space for future charging models and value-added services, including but not limited to more refined subscription tiers, advanced strategy suggestions, and customized labeling data. However, at this stage, the official has not disclosed key metrics directly related to this AI trading feature, such as real trading volume, user growth, or conversion rates. In the absence of quantitative data support, external parties can only make directional judgments about its commercial effectiveness and revenue contribution, remaining more at the logical level of "the model has potential" rather than concluding on actual profitability.

Evolution Path from Experimental Field to Potential Paradigm

With the launch of this AI trading feature, Nansen has taken a key step from being a pure data provider to an integrated trading entry, beginning to layer execution and cross-chain capabilities on top of its own labeling system and on-chain analysis advantages, attempting to position itself at the intersection of information and liquidity. Currently, the functional network coverage is limited to the Solana and Base public chains and is still in the Pro user testing phase, which significantly constrains its actual impact on a broader user base and the overall market structure in the short term, resembling a "small-scale experimental field" aimed at early adopters and high-value users. Looking ahead to the evolution path, if Nansen expands AI-driven trading to more public chain ecosystems and gradually relaxes permission boundaries while exploring more refined user layering and risk control mechanisms within a compliance framework, then conversational order placement is expected to change the interaction between retail investors and on-chain liquidity on a larger scale, while also potentially attracting more regulatory attention regarding AI decision transparency and accountability. For users interested in this direction, it will be important to closely observe Nansen's official disclosures regarding usage data, trading scale, user retention, and product iteration pace to verify the extent to which the current market narrative around "AI + on-chain trading" can translate into sustainable product value and commercial returns.

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