On March 10, 2026, Jack Yi, founder of Liquid Capital, publicly stated that the drop of ETH below the $2000 region represents a mid-to-long-term bottoming window, linking this judgment to “AI is pushing the industry into a critical window for transforming and restructuring Crypto.” This statement shifts the discussion about Ethereum prices from a mere bull-bear cycle back to the larger narrative of a technological paradigm shift. On one side is the relatively stable cryptographic bull-bear rhythm over the past decade, while on the other is the steep rise of AI capabilities in the last two years—these two timelines overlapping at the 2026 node raises the question: Is the traditional cycle model misaligned with the new rhythm brought about by the acceleration of AI technology, and is ETH below $2000 really a cycle low or a 'historical undervaluation' before the technological dividend?

The Time Lag from the Last Bull-Bear Cycle to the AI Explosion

● Cycle Reference Frame: Looking back at the last complete bull-bear cycle, Ethereum skyrocketed from a few hundred dollars to historical highs before falling back to the mid-low range, with price and narrative highly intertwined—from ICO to DeFi Summer, and then to the NFT boom, each wave of application has been accompanied by a surge in Gas prices and a reassessment of valuations. Today, “below $2000” is viewed as a potential bottom, built upon the historical memory of ETH repeatedly retracing over 70% from high points in past cycles and subsequently reaching new highs.

● Macroeconomic and Valuation Constraints: In the previous cycle, interest rate hikes and tightening liquidity in the U.S. forcibly interrupted the euphoria of the crypto market, causing ETH's valuation to suffer a systemic crash despite no significant deterioration in its fundamentals. Traditional cycle theories emphasize variables such as funding costs, risk appetite, and halving rhythms, creating a “downward pull” on the pricing of assets like ETH, leading the market to define the so-called “bottom range” through historical retracement ratios and macroeconomic replications, which limits the imagination of structural changes brought about by new narratives.

● AI Timeline Overlap: Unlike the previous cycle, this round of ETH adjustments occurs after the explosion of large model and Agent capabilities. After 2023, AI expanded from content generation to automated decision-making and execution, with the onset of AI Agents starting to actively interact with the on-chain world in 2025-2026. That is to say, when ETH is priced by the market according to the “traditional bull-bear script,” it coincides with the acceleration of AI technology in computational power, reasoning, and automated execution, with the cyclical pullback and technological explosion highly overlapping in time, laying the groundwork for the narrative of "transforming Crypto during the window period."

Jack Yi's Bet: The Coupling of ETH's Low Point and AI Window

● Bottoming Logic Boundaries: Jack Yi views ETH below $2000 as a mid-to-long-term bottoming area, not merely based on price anchoring, but on several underlying assumptions: First, the macroeconomy has not entered a systemic recession; although liquidity is tight, it is reversible; second, Ethereum still holds influence in the public chain competition, and the L2 ecosystem has not been overturned; third, new use cases driven by AI will gradually amplify the demand for ETH within the next one to two cycles. Under these assumptions, the current price seems more like a discount of the “old narrative premium” rather than a pricing of the “new narrative dividend.”

● AI Window Expectations: He proposes that “the speed of AI evolution has pushed the industry into a window period for transforming and reconstructing Crypto,” which implicitly indicates a reassessment of infrastructure and user behavior: On one hand, AI requires a safe, transparent, and executable environment, viewing smart contracts, Rollups, privacy computing, and other infrastructures as “AI's external brain and accounting system”; on the other hand, users in the future will not interact with contracts directly but will complete asset allocation, payments, and strategy execution in bulk through AI Agents, signifying a leap in on-chain interaction frequency, complexity, and automation level, with ETH's role as the underlying Gas and collateral asset being amplified again.

● Resonance and Divergence: This perspective has caused a noticeable emotional stratification among institutional and developer communities. Some long-term bulls believe that AI + Crypto is the next main line after DeFi and NFT and are happy to accept the notion that “below $2000 is a strategic allocation area”; however, there are also cautious voices pointing out that current AI applications largely remain at the demonstration level, with actual on-chain transaction volume and fee contributions being limited, making it difficult to support the determinism of “historical undervaluation.” Developers are more concerned about toolchain maturity, privacy, and compliance issues, while traditional funds are more focused on whether ETH still has deep excavation space, resulting in a misalignment in time dimensions and risk appetites.

AI Agents on Chain: OpenClaw and Bank of AI as Experimentation Grounds

● Automatic Interaction Scenarios: Taking OpenClaw and other AI Agents as examples, they have begun to attempt to automatically initiate, sign, and execute on-chain transactions without human intervention. A typical path involves the Agent making decisions based on external data and preset strategies, managing private keys or signature permissions through a security module, and interacting with DEXs, lending protocols, or contract interfaces. Such scenarios make "robots as main accounts" a tangible prototype, no longer just human clicks on wallet buttons, but allowing AI to execute complex strategy combinations on-chain.

● Payment and DeFi Closed Loop: Surrounding projects like Bank of AI, AI is attempting to establish a complete closed loop from “instructions to payment to DeFi configuration.” Users need only express their demands in natural language, and the Agent can call stablecoins like USDT and USDC to complete payments, investments, or strategy follow-ups, settling and managing assets through Ethereum and its L2. In this system, stablecoins become the value carriers for AI payments, while the Ethereum network serves as the foundational layer for settlement and contract execution, providing accounting and security for each AI "consumption" and "investment."

● Grounding Temperature and Limitations: However, these early applications are still in the experimental stage: in terms of usage thresholds, users need to understand permission authorizations, risk parameters, and even multi-chain bridging; regarding frequency of use, truly fully unattended large-scale scenarios remain rare, most are limited to small-scale tests and enthusiast circles; in terms of security, problems such as excessive Agent authorization and insufficient contract auditing amplify the risks of errors and attacks. AI has indeed begun to reshape on-chain interaction methods, but more as an "experimental runway," and it will take time to substantially alter Ethereum's fee structure and transaction volume.

Infrastructure Underflows: Trust, Compliance, and Systemic Risks

● Verifiable Computing Power and Privacy: Progress in Phala's collaboration with Intel has made the three major challenges that AI + Crypto must face to scale clearer: First, the computing power must be trustworthy and must prove that a certain inference or training indeed occurs in designated hardware and environments; second, data privacy—AI's learning and calling of sensitive user data must leverage trusted execution environments, zero-knowledge solutions, etc., to avoid leaks; third, execution must be verifiable, where on-chain participants need to statistically verify the correctness of AI outputs without reproducing complete calculations, which all determines whether Ethereum and its sidechains can bear “AI-level” computing power and data flow.

● Amplifying Effects of Underlying Vulnerabilities: The incident where the Cosmos EVM Stack vulnerability affected the Saga blockchain serves as a cautionary reminder to the market—when AI automatic interactions scale up, any security flaws in contracts and the underlying stack may be exponentially amplified. AI Agents do not have “instinctive vigilance”—they will mechanically execute preset logic, and if exploitable vulnerabilities exist in the underlying environment, automated fund flows can multiply losses in a very short time. For teams attempting to deploy AI Agents in a multi-chain environment, the Saga event serves as a risk sample regarding auditing and choice of underlying stacks.

● Asset Custody and Transparency: Meanwhile, the Eigen Foundation clarified the nature of EIGEN token transfers, which, although not changing the core parameters of token economics, again highlights the importance of custody structures and information disclosure against the backdrop of AI-related fund flows accelerating into the industry. As increasingly more AI-related funds enter the Ethereum ecosystem through funds, DAOs, or protocols, the transparency of token transfers, staking, and unlocking will directly affect whether institutions are willing to layer additional risk exposure on ETH, consequently impacting the market's trust in the notion of “historical undervaluation.”

When AI Meets Ethereum: The Reshaping of Valuation Anchors

● New Use Cases and On-Chain Indicators: From a valuation perspective, if AI-driven new use cases are to support the idea of ETH being in the “bottom interval,” they must leave a significant track in transaction volume, Gas consumption, and L2 activity. AI Agents' behaviors such as high-frequency ordering, automatic settlement, and batch payment have the potential to increase the basic transaction density of the Ethereum mainnet and Rollups, prompting demand for more complex contract calls. Although current on-chain data has yet to show an independent segment of “pure AI traffic,” the sustained high levels of active addresses and contract calls on the mainnet and mainstream L2 in 2025-2026 also provide a space for imagining future AI traffic overlay.

● Stablecoin Payment and Settlement Demand: As the executing entity, AI naturally prefers to use stablecoins like USDT and USDC as the unit of valuation and payment, with settlement and clearing predominantly occurring on Ethereum and its L2. This means that as AI payments transition from experimental to scalable, Ethereum's role in the settlement layer will further solidify, and the endogenous demand for ETH as Gas and collateral assets will be passively pushed up—not only due to the increased number of transactions but also because more protocols will choose ETH as the center of safety and liquidity, thus providing structural support to its valuation anchor.

● Value Seen from Track Penetration Rates: In the foreseeable future, it is neither feasible nor prudent to make precise predictions about the returns or payback periods of any single AI + Crypto project, nor does the current data support specific yield multiplication for projects like OpenClaw or Bank of AI. A more prudent perspective is to assess from the overall track penetration rates and fee capture capabilities: whether the share of AI traffic in Crypto continues to rise, and whether Ethereum and mainstream L2 can capture corresponding Gas and MEV revenues—these structural data are crucial for assessing ETH's long-term value space, rather than short-term price fluctuations.

Window Period and Valuation Killing: The Tension of the ETH Bull Narrative

● Confluence of Bull Narratives: In summary, Jack Yi's view on bottoming ETH and the mainline narrative of “AI restructuring Crypto” overlap within the time window of 2026, forming one of the most attractive bullish stories in the current market: In pricing terms, ETH fluctuates around $2000, providing space for long-term funds to build positions in batches; in narrative terms, AI Agents, trustworthy computing power, and a closed loop for on-chain payments provide imagination for a “new demand curve.” This misalignment of price pullback and expectations of technological dividends becomes a psychological fulcrum for bulls daring to increase their positions amidst pessimistic sentiments.

● Risks and Uncertainties: However, it must be emphasized that the publicly available information does not include specific bottoming time nodes and funding scale arrangements, nor does any institution provide a reliable lower limit for ETH's downside potential. Macro-level policy tightening, unexpected security incidents, underlying vulnerabilities, and sudden regulatory environment shifts could still impose additional valuation-killing pressure on ETH, rendering “below $2000” more akin to an interval with partially improved medium-to-long-term risk-reward ratios rather than a bottom line indicating that market sentiment will not continue to decline.

● How to Observe “Historical Undervaluation”: Looking forward to the next one to two cycles, the evolutionary path of AI + Crypto will likely undergo a gradual process of “tool maturity—scenario formation—fee expansion.” To determine whether ETH is genuinely in a state of “historical undervaluation,” several key indicators can be monitored: first, whether the proportion of AI-related contracts called on Ethereum and mainstream L2 continues to rise; second, whether the usage and settlement frequency of stablecoins in AI payment scenarios significantly boost on-chain Gas and cross-chain traffic; third, the deployment density of trusted computing power and privacy computing infrastructures like Phala on the mainnet and sidechains, and whether security incidents show a declining trend. These long-term, structural signals can better answer the question of “whether ETH below $2000 is worth it” than any short-term market fluctuation.

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