Nansen rolls out autonomous AI crypto trading on Base and Solana, pushing its platform beyond analytics and into fully integrated trade execution aimed squarely at everyday traders.
Instead of staring at complex charts or juggling order books, users can now open Nansen’s mobile app and type natural language commands such as “Buy $200 of SOL if the price dips 3% in the next hour” or “Swap my USDC to the top trending memecoin on Base.” The company’s new AI agents interpret these conversational prompts, analyze onchain data, and execute trades on behalf of the user, while still leaving final confirmation and control in the user’s hands.
The launch marks Nansen’s first major step from being primarily a blockchain analytics provider to becoming a full-stack trading interface. For years, the platform built its reputation by labeling and tracking hundreds of millions of blockchain addresses, surfacing what it calls high-quality onchain signals: smart money flows, large whale movements, early token trends and liquidity shifts. Now, those same signals are being wired directly into an AI layer that can turn insights into executable trading actions in real time.
Nansen describes this new experience as “vibe trading” – a blend of data-driven analytics and automated execution, presented through an interface that feels more like chatting with a digital assistant than operating a professional trading terminal. The idea is to lower the barrier to entry for retail users who may be intimidated by technical tools, while still giving them access to the type of information and execution standards typically reserved for more advanced traders.
At launch, the AI trading feature supports two networks: Coinbase-incubated Base and high-throughput chain Solana. These blockchains were chosen for their low fees and fast confirmation times, making them suitable environments for rapid-fire, AI-assisted strategies. Nansen has already indicated that support for additional networks is on the roadmap, with cross-chain infrastructure partners in place to ease expansion.
Under the hood, the AI interface pulls from Nansen’s proprietary onchain database, which the company says spans hundreds of millions of labeled addresses across major networks. This labeling includes categories such as market makers, funds, exchanges, smart money wallets and known insiders. By feeding this structured, finance-specific dataset into its AI layer, Nansen aims to avoid the common pitfalls of generic large language models that were never trained for real-time trading decisions.
Unlike general-purpose AI assistants that might hallucinate prices or suggest trades based on outdated or incomplete market data, Nansen’s system is designed to anchor every suggestion to verifiable onchain activity. In practice, that means an AI agent could, for example, warn a user that a token they want to buy is being heavily sold by smart money wallets, or highlight that liquidity is concentrated on a specific decentralized exchange, affecting slippage and execution risk.
To make cross-chain trading seamless, Nansen has integrated with a set of established liquidity and bridging providers. Decentralized aggregator Jupiter, centralized exchange infrastructure from OKX, and cross-chain protocol LI.FI are all plugged into the backend, allowing the AI to route orders between Base and Solana and, in the future, additional blockchain networks. This routing is abstracted away from the end user, who only sees a simple conversational interface and a final quote before confirming a trade.
Trading itself is executed through the embedded Nansen Wallet, which is built on top of Privy’s self-custody technology. Users retain control of their keys and assets, while the AI agent operates as a sophisticated assistant that can propose, structure and prepare transactions, but cannot finalize them without explicit approval. This model is intended to combine automation with a security layer that prevents unauthorized or runaway trading behavior.
Due to regulatory requirements, Nansen’s autonomous trading tools will not be available everywhere. At launch, residents of several jurisdictions, including Singapore, Cuba, Iran, North Korea, Syria, Russia and certain regions of Ukraine, are excluded from access. The company cites compliance and local restrictions as the main reasons for these limitations, aligning with a broader trend in crypto where AI and advanced trading services roll out first in permissive markets.
The timing of Nansen’s move reflects a broader industry shift toward AI-assisted investing. Across the crypto ecosystem, teams are racing to build agents that can scan markets 24/7, identify patterns humans might miss, and compress complex strategies into simple commands. Conversational trading is emerging as a major theme: instead of learning advanced technical analysis, users describe their goals, and the system handles the translation from intent to executable strategy.
Yet, recent experiments have also highlighted that not all AI is created equal for trading. In a high-profile autonomous trading competition held in late 2025, several lower-cost Chinese models were reported to have outperformed more famous Western systems on crypto trading tasks. Models such as QWEN3 MAX and DeepSeek reportedly delivered stronger quantitative results than multiple widely known chatbots, with QWEN3 being the only participant to achieve positive returns over the testing period. The outcome underscored the challenge of building AI that can handle real-time markets, risk, and execution – and suggested that specialized optimization can matter more than brand recognition.
These tests also revealed a persistent gap between conversational competence and trading performance. Many large language models can explain market concepts or summarize news, but struggle with latency-sensitive decision-making, portfolio management, and dynamic risk control when real money is on the line. Nansen’s strategy is to bypass this limitation by coupling a domain-aware AI interface with a deep, curated onchain dataset and purpose-built execution infrastructure, rather than relying on a generic chatbot to do everything.
For retail traders, the potential benefits of this approach are significant. Instead of flipping between multiple apps to research wallets, view token metrics, and place trades, they can stay within a single environment that pulls onchain intelligence and execution into one flow. An investor might ask: “Show me tokens on Solana that smart money has accumulated over the last week with at least 1 million dollars in new inflows, then allocate 5% of my portfolio evenly across the top three,” and the system can do the heavy lifting of screening, filtering, and structuring orders.
At the same time, the introduction of AI-driven “vibe trading” raises important questions about responsibility and risk. There is a fine line between making markets more accessible and encouraging users to over-rely on automated suggestions. Nansen emphasizes that users remain in charge and must approve every trade, but as the interface becomes more conversational and friendly, it may be easy for newcomers to forget that they are dealing with highly volatile assets and complex market dynamics.
From a regulatory and ethical standpoint, AI trading tools will likely come under increasing scrutiny. Questions around transparency, explainability and potential bias in trading recommendations are already emerging in traditional finance. Crypto-native platforms like Nansen will have to navigate these issues while operating in an environment that is often less clearly regulated, particularly when trades span multiple jurisdictions and chains.
Another key dimension is education. If conversational interfaces are to succeed without simply turning into slot machines with an AI skin, they will need to help users understand why certain suggestions are being made. That could include explaining which wallets influenced a particular signal, how liquidity conditions affected route choice, or what risk assumptions underlie an allocation proposal. Building explainable AI around trading could become a differentiator for platforms competing in this new space.
For active traders, Nansen’s move hints at a future where the “trading terminal” is largely invisible. Instead of elaborate screens filled with indicators, order books and custom scripts, much of the complexity may be tucked behind a chat window driven by agents tuned for different strategies: momentum, arbitrage, long-term accumulation or yield optimization. Users might toggle between agents or combine them, delegating parts of their portfolio to different AI personas with predefined constraints.
Institutional and professional users could also find value in such tools, even if the initial positioning is toward retail. Onchain-focused funds, market makers or quant teams might plug Nansen’s labeled address data and AI filtering into their own proprietary systems, using the conversational interface mainly as a control and monitoring layer. Over time, an ecosystem of specialized agents could emerge, each designed for specific networks, sectors or risk profiles.
Looking ahead, the expansion beyond Base and Solana will be a crucial test. Supporting additional chains means handling fragmented liquidity, varying fee markets, different execution risks, and a much larger attack surface for potential exploits. How smoothly Nansen’s AI can abstract away this complexity while preserving security and performance will determine whether the product becomes an industry staple or remains a niche experiment.
What is clear is that the line between analytics and execution in crypto is fading fast. Platforms that once merely reported on what was happening onchain are now building tools to act on that information in real time. Nansen’s AI trading rollout is a prominent example of this convergence: a data company turning its insights into an action engine, packaged in a conversational interface that aims to bring sophisticated crypto trading within reach of anyone with a phone and a basic understanding of how to give instructions in plain language.