Is the divergence in macroeconomic views intensifying? Gate for AI verifies market signals using on-chain data

Entering the second quarter of 2026, the global macro analyst community’s views on the direction of the crypto market have shown an uncommon divergence in recent years. The core disagreement centers on a logical chain: the Federal Reserve’s interest rate path, global liquidity trends, and the role of crypto assets within this framework.

By 2025, the Federal Reserve has completed three rate cuts, bringing the benchmark interest rate down to a range of 3.5% to 3.75%, yet it remains at an 18-year high. The latest dot plot shows serious disagreement among Fed officials regarding the interest rate path in 2026, with opinions on zero, one, or two rate cuts nearly evenly split. The market’s debate over the pace of rate cuts has shifted macro analysis from “consensus-driven” to “divergence-driven.”

Simultaneously, geopolitical variables have repeatedly disrupted the landscape. After a brief ceasefire, tensions in the Middle East have escalated again, with oil price fluctuations and rising inflation expectations pushing the market into a state of high uncertainty. Some analysts favor the safe-haven attributes of crypto assets, while others emphasize that overvalued assets will be the first to suffer in a liquidity contraction. As of April 13, 2026, Gate market data reports Bitcoin at approximately $71,216.20, with a 24-hour change of -0.62%; Ethereum at approximately $2,203.29, with a 24-hour change of -0.68%. The market has been oscillating between $70,000 and $72,000, with clear disagreements between bulls and bears.

Against this backdrop, traditional macro narrative frameworks are no longer sufficient to support trading decisions. An increasing number of market participants are shifting their focus from “what others say” to “how funds move”—on-chain data is becoming a key verification tool to bridge macro analysis disagreements.

Gate for AI: Connecting a Unified On-Chain Data Access Point

In March 2026, Gate officially launched Gate for AI, a unified interface for calling capabilities tailored for AI agents. Its core positioning is not just a single market data query or order placement aid, but a comprehensive protocol encapsulation of the core capabilities of centralized exchanges and on-chain trading, enabling AI to participate directly in the entire process—from data analysis and strategy generation to order execution and review.

Architecturally, Gate for AI adopts a dual-layer capability structure: MCP (standardized tool interface) and Skills (pre-orchestrated advanced capability modules). MCP provides standardized basic interfaces covering market data, account information, trade execution, and on-chain data queries, allowing AI to quickly access and invoke platform capabilities; Skills encapsulate higher-level strategy modules, such as market scanning, position range evaluation, and risk analysis.

For macro analysis, the most valuable module of Gate for AI is “multi-dimensional on-chain data.” This module connects the ability to query across currencies, projects, addresses, and risk information within a single interface system. This means users can complete the entire process—from capturing on-chain signals to trend judgment—within a unified environment, without switching tools. The integration of information directly shortens the time from data to insight.

Verification Logic of On-Chain Data

Price movements can be misleading, but on-chain data rarely lies. During phases of conflicting macro views, on-chain data provides market participants with an independent, objective path outside of analyst judgments.

A systematic on-chain data interpretation framework typically includes three levels. The first is cycle positioning: markets follow a financial cycle of “macro liquidity → asset bubbles → rate hikes and contractions → market clearing,” with the current (2026) phase at the end of rate hikes, in a period of oscillation and recovery amid rate cut expectations. On-chain signals serve as verification tools: beyond price fluctuations and macro statements, fund flows, address behaviors, and exchange deposit data form verifiable independent variables. Sentiment indicators are auxiliary: at extremes, markets often diverge—extreme fear may indicate a potential accumulation window, while extreme greed may signal distribution risk.

Based on the on-chain data perspective provided by Gate for AI, here are several quantifiable signals observed in recent markets.

Signal 1: The Distinction Between “Reflow” and “Sedimentation” of Stablecoins

Stablecoins are proxy variables for “deployable funds” within the crypto ecosystem, and their flow changes are often seen as forward-looking indicators of market sentiment. In March 2026, on-chain data shows that net stablecoin flows into major centralized exchanges turned positive, with about $2.4 billion flowing back, indicating a reversal in capital flow trends in the crypto market.

However, interpreting this data is not straightforward. Despite large stablecoin inflows to trading platforms, spot trading volume has sharply contracted from a peak of $81 billion to about $3.5 billion, a decline of over 95%. Funds are entering the market, but trading activity has not kept pace. This “funds in place but action stalled” state reflects a market in a wait-and-see period with unclear direction.

A larger structural change is that stablecoins are “leaving exchanges but not leaving the market.” As of March 13, 2026, the total global stablecoin market cap reached a record high of approximately $320.9 billion, yet reserves on many major trading platforms are continuously net outflowing. Funds are migrating from exchange wallets to on-chain yield protocols and self-custody wallets—platforms like Aave, Compound, and Morpho offer annualized yields of 3% to 8%, allowing funds to appreciate without passing through exchanges.

This means that simply equating “stablecoin inflows into exchanges” with “bullish signals” is no longer accurate. The granular on-chain data provided by Gate for AI helps users see through surface inflow numbers, observe whether funds are truly converted into holdings, and whether outflows are entering yield-generating on-chain scenarios. Cross-validation across multiple dimensions is the core value of on-chain data analysis.

Signal 2: Whales’ Behavior Revealing Supply-Demand Structures

During periods of macro view divergence, observing the behavior of the largest address groups (whales) is often more valuable than focusing on short-term price fluctuations. On-chain data shows that addresses holding over 100 BTC or ETH have been continuously accumulating during recent market oscillations, a behavior historically associated with bottom-region chip collection.

Identifying whale behavior requires multi-source data cross-validation. Typical accumulation features include: during price declines or consolidations, exchanges show persistent net outflows while whale address balances increase. Conversely, during price rises or stagnation, large net inflows into exchanges and decreasing whale balances are observed. In extreme market conditions, single large transfers can be noisy; focus should be on cumulative trends over 24 hours and behavior changes in the top 1% of addresses.

Geopolitical tensions further confirm this pattern. Data shows that during the breakdown of US-Iran negotiations and the subsequent Bitcoin price decline, the largest whale addresses did not follow the sell-off but instead showed net inflows. The trading logic of large investors versus retail traders is amplified at such moments: the former focus on scarcity and hedge against the global monetary system, while the latter are more influenced by short-term sentiment and price swings.

Gate for AI’s address analysis capabilities enable users to track large addresses’ fund flows, holding durations, and behavior patterns, providing insights into the true supply-demand structure from “who is buying” and “who is selling.”

Signal 3: AI Narrative Reversion and Capital Sector Rotation

Entering the second quarter of 2026, on-chain data has captured a notable sector rotation signal. According to monitoring in the first week of April, the AI sector has become a key focus for capital reflow. Interactions between addresses of projects like Bittensor (TAO) and Virtuals Protocol (VIRTUAL), as well as DEX trading volumes, have significantly increased, entering the top five abnormal signal tokens list. This phenomenon is set against the backdrop of Solana’s total locked value surpassing previous peaks to reach an all-time high, indicating that market liquidity is shifting toward high-performance infrastructure capable of supporting high-frequency AI agent interactions.

As of April 10, 2026, Gate market data shows TAO at $271.80, with a 24-hour trading volume of $12.47 million and a circulating market cap of $2.63 billion. Notably, TAO experienced about a 15% price correction within 24 hours, but over a 30-day period, it still maintained a substantial 39.78% increase, with upward momentum intact on a monthly basis. VIRTUAL is priced at $0.6713, with a 24-hour trading volume of $582.46K and a market cap of $441.62 million, showing strong resilience (+3.37%) over 24 hours.

The increased on-chain activity in the AI sector is closely related to the declining costs of large language model applications and surging demand for edge computing in 2026. “Decentralized compute supply” is moving from proof-of-concept to initial commercial exploration, and early on-chain signals provide quantifiable support for the “application-driven” macro narrative.

Building a Cross-Validation Framework Between On-Chain Data and Macro Analysis

In phases of macro divergence, conclusions based on a single dimension often lack sufficient persuasiveness. Combining macro cycle positioning with on-chain data verification allows for cross-confirmation across multiple information sources, improving judgment robustness.

Here is a reference cross-validation framework:

Macro liquidity expectations shift + market sentiment extreme fear + whale accumulation ongoing + derivatives leverage unwinding: This combination often appears near market bottoms, where funds transfer from weak hands to strong hands amid panic.

Macro tightening expectations rise + market sentiment extreme greed + whale hidden distribution + exchange sell walls thickening: This combination typically occurs near market tops, with funds gradually distributing chips amid euphoria.

Contradictory macro data + chaotic on-chain signals + price oscillation without trend: This indicates an unclear market direction, and patience for clearer signals is often the more prudent choice.

Gate for AI, via MCP standardized interfaces, consolidates market data, address activity, exchange fund flows, and other multi-dimensional data into a single platform, greatly reducing the operational cost of cross-data-source validation. Users can complete the entire process—from data collection to signal judgment—in one environment without switching tools.

Beyond Data: The Value of Information Integration

The crypto market is highly information-dense. The 24/7 operation makes it difficult for human traders to continuously track market dynamics, and the fragmentation of information sources such as on-chain data, technical indicators, and community sentiment further complicates decision-making.

Gate for AI’s “multi-dimensional on-chain data” module is designed to address this issue. By integrating currency data, project info, address behaviors, and risk alerts into a unified interface, it helps users focus on key signals amid information overload, avoiding getting lost in vast data.

Moreover, Gate for AI not only serves manual analysis but also provides a foundational infrastructure layer for AI agents. After integrating mainstream AI models, AI agents can perform institutional-level data integration and process operations, including multi-source data fusion, risk assessment, position calculation, and result tracking. This signifies an evolution from “manual judgment” to “human-machine collaboration” in on-chain data interpretation.

Conclusion

Macro divergence will not disappear. When traditional macro frameworks struggle to produce consistent answers, on-chain data offers an independent verification dimension beyond viewpoints. Prices can fluctuate, sentiment can sway, but fund flows, address behaviors, and exchange deposit data form traceable, verifiable objective facts.

Gate for AI, as a unified portal connecting on-chain data, market prices, and trade execution, provides a platform for information integration and signal verification. It does not make directional judgments but supplies the data foundation needed for decision-making.

Positioned within macro cycles, verifying behaviors with on-chain data, and thinking contrarily during extreme sentiment—this may be an effective path to maintaining clarity amid increasing market divergence.