On March 26, 2026, Yi Lihua, founder of Liquid Capital (formerly LD Capital), voiced concerns on X, intertwining geopolitical tensions, cryptocurrency market fluctuations, and AI technology reforms into a single narrative thread. This VC, who has long been active in both primary and secondary markets, serves as both a fund organizer and an emotion amplifier in the industry, with his public judgments often regarded as significant references for cycles and directions. Discussing the potentially escalating war situation, he talked about asset hedging and valuation compression; regarding opportunities after steep declines, he mentioned “there will definitely be a great bottom-fishing opportunity this year”; he also characterized the impact of AI on efficiency as “the magnitude change of going directly from the era of carriages to the era of airplanes.” In the context of geopolitical uncertainty, market downturns, and accelerating technology, he attempts to answer the same set of questions: How to price risk between the two largest variables of war and AI, how to filter opportunities, and how industries will be reconstructed. This article unpacks how a VC reconstructs their investment coordinate system between fear and greed along this main thread.

Fire Approaching the Screen: Capital Nerves Under Geopolitical Conflict

In late March 2026, tensions surrounding the United States and Iran intensified once again. According to the Tasnim News Agency, Iran has responded to the 15-point proposal presented by the U.S. through intermediaries, which includes conditions such as ceasefire and war reparations, but specific details of the terms have not been made public. This article does not extend into these undisclosed diplomatic technicalities, but rather views them as macro context: the negotiations that sway between ceasefire and escalation are enough to cast a shadow over the pricing of global risk assets.

For cryptocurrency assets, geopolitical uncertainties usually transmit through several pathways. Firstly, there is an emergence of hedging sentiment, where funds temporarily withdraw from high-volatility assets to traditional safe havens like cash and U.S. Treasuries, directly compressing risk appetite for cryptocurrencies and related U.S. stocks; secondly, there is a downgrade in growth and liquidity expectations; if the market worries that conflict escalation will undermine global trade and energy prices, expectations about the monetary pathways of the Federal Reserve and other central banks will be adjusted, subsequently changing the discount rates for tech growth assets; thirdly, there are regulatory and compliance variables; in extreme cases, sanctions and cross-border payment restrictions may also alter the pathways of on-chain funds. Yi Lihua discusses war, not out of geopolitical interest itself, but being sensitive to the chains that translate emotions and expectations into asset prices.

This uncertainty often manifests itself as tug-of-war and divergence in the market. Some funds choose to get off the bus early, handing over chips to players willing to endure volatility; others, based on the judgment that “war will not escalate comprehensively,” attempt to position themselves early beneath the looming clouds. Social media sentiment, spurred by extreme headlines and fragmented information, tends to swing between the extremes of “the end of the world” and “a great bottom-fishing opportunity.” It is within this contradictory environment that the notion of a “great bottom-fishing opportunity” brings both hope and raises the psychological thresholds of market participants: will it be a matter of weathering a storm or being caught in the next stampede?

Bloodied Day in the Crypto Sphere: A Harsh Contrast of U.S. Stock Token Prices

This sentiment has been intuitively reflected in U.S.-listed cryptocurrency tokens. According to Bitget data, during a 24-hour period intertwined with geopolitical tensions and market adjustments, the cryptocurrency sector’s U.S. stock tokens showed clear divergence: GEMI down 10.55%, IREN down 6.86%, COIN down 5.44%, CRCL down 3.72%, while fellow sector member MARA increased by 1.96%. Within the same time window, different targets within the same sector exhibited polarities of hot and cold; this “bloodied day” is often not driven by a single fundamental event but rather rapid reallocation of funds between different risk exposures.

If we line up this set of data into a sequence, we can see who is being concentratedly sold off and who is viewed as a relatively safe “safe haven.” The deeper declines of GEMI, IREN, COIN, and CRCL carry more worries about on-chain business, mining, and transaction volume expectations: when the market anticipates reduced trading activity and increased regulatory pressure, these targets closely tied to business cycles likely become the first victims of concentrated sell pressure. In contrast, MARA’s ability to still record a +1.96% increase in the same emotional environment reflects structural migration of some funds within the sector rather than a complete withdrawal: rather than cutting losses across the board, it is preferable to bet on a select few targets deemed to have a more stable balance sheet and clearer cost structures.

From the perspective of sentiment and trading behavior, this round of declines contains characteristics of both panic selling and rational reduction and adjustment. On one hand, the consecutive double-digit declines indicate that short-term leverage and weak hands have been cleaned out, showing typical “liquidity crunch” properties; on the other hand, MARA’s counterperformance suggests that not all funds are caught in indiscriminate selling, with some participants using price signals to reassess individual stock risk exposures. This combination of “the sector as a whole being under pressure, with relative price revaluation internally” raises the suspense of “whether we are approaching a bottoming-out zone”: once the panic subsides, will funds switch from defensive stances to selective aggression?

This Year Will Definitely Have a Big Pit: The Great Bottom-Fishing Opportunity in Yi Lihua's Eyes

“This year will definitely have a great bottom-fishing opportunity,” this statement appeared in a discussion on X on March 26, 2026, and the context is not a precise prediction of a specific price point but a judgment on the outcome after the interweaving of cycles and macro uncertainties. Yi Lihua did not provide specific timeframes, price ranges, or technical indicators, but rather placed the risks of war, liquidity fluctuations, and the industry's own accumulated bubbles on the same chart: when these forces overlap at a certain moment, the market is highly likely to experience a “deep pullback + liquidity mismatch” big pit.

In his narrative, geopolitical conflicts are the first hammer compressing valuations: as long as the ceasefire and escalation remain unresolved, risk premiums are unlikely to drop; the second hammer arises from the swing in liquidity expectations, whether it’s the repricing of the Federal Reserve’s path or the reassessment of global fund risk appetites, all amplify volatility through discount rates and risk premium structures; the third is the bubble digestion within the industry—the high valuations and unprofitable projects generated in the past bull market ultimately require a moment of “unified liquidation.” Such a big pit, in his view, is not a disaster but a structural buying point: the more intense the erroneous killings, the more it can pull back the valuations of low-quality assets and high-quality assets to rational ranges, leaving clearer entry coordinates for long-term funds.

From the VC perspective, “bottom fishing” more signifies valuation resets rather than short-term bounce speculation. What Yi Lihua emphasizes is to focus on chain infrastructure, leading protocols, and high-quality targets with real cash flow and product stickiness during the potentially coming deep pullback, waiting for them to be discounted by market sentiment collectively, rather than attempting to “scoop every short bottom” through high-frequency trading. In the primary market, lower valuations and more stringent terms will reshape the negotiation structure between founders and capital in a winter. Under such logic, accurately “timing the points” is not important; what matters is establishing a safety margin that one can afford in the dimensions of valuation—quality—time.

It should also be emphasized that he has not provided any precise operational guidelines, nor set a quantitative standard for “how far down is a great bottom-fishing opportunity,” nor provided technical indicators for use in conjunction. Any attempt to mechanically translate this statement into a “next full position signal” would deviate from his original abstract judgment stemming from macro and cyclical considerations. For ordinary investors, a more reasonable way to use it is to treat it as a psychological expectation management for large fluctuations, rather than as a fine directive for adjusting leverage and positions.

From Carriages to Airplanes: How AI Rewrite Investment and Entrepreneurship

When discussing AI, Yi Lihua used a highly visual metaphor: “the magnitude change of going directly from the era of carriages to the era of airplanes.” The transition from carriages to cars is a linear improvement in speed and efficiency; the transition from carriages to airplanes fundamentally rewrites our perception of space and time—you are no longer just “running somewhat faster,” but have routes and methods previously unimaginable. In the context of VC and the cryptocurrency industry, this statement points to: what AI brings is not simple cost reduction and efficiency improvement, but rather a leap in production relations and organizational forms.

From the perspective of investment institutions, the potential applications of AI in research, risk control, product development, and operations are being reassessed: on the research end, models can mine dispersive signals from vast on-chain data and macro information, assisting in project screening and risk profiling; in risk control, anomaly pattern recognition for DeFi protocols and on-chain fund flows can give early warnings for governance attack risks like those at Moonwell, instead of only a retrospective analysis; on the product and operational side, AI enables small teams to support global user services and product iterations with minimal manpower, compressing complexities that only large enterprises could handle in the past down to levels manageable for entrepreneurial teams. This is also why more and more institutions view AI as a new round of infrastructure revolution—whoever can integrate it into their “investment—construction—operation” closed loop first may grasp the rhythm in the next cycle.

In such a context, capital's implicit and explicit demands on project parties to “deeply use AI or be eliminated” are evolving from being a bonus item to a “qualifying threshold.” The so-called “depth” is no longer just stating a few AI keywords on the website but whether the team's daily workflow, contract audits, user growth, community operations, etc., truly embed automation and intelligent decision-making; the so-called “threshold” means that, under the same sector and valuation levels, teams that do not use AI will find it hard to convince capital that they possess sufficient iteration speed and survival capability. This trend is quietly reshaping the capability structure of entrepreneurial teams:

Founders now need not only to understand industry logic and token economics but also to grasp model capability boundaries and data construction paths; the tech stack has expanded from traditional Web3 toolchains to model invocation, vector databases, and automated testing and deployment; organizational collaboration methods are shifting from linear division of labor to human-machine collaboration, “one person + a group of agents” becoming the new basic unit; and the trial-and-error speed is continually elevated with the help of AI, where version verifications that used to take weeks can now be completed within days. For teams daring to embrace this change, AI acts as an amplifier; for teams trying to cling to old production methods and merely label their presentations with buzzwords, AI might highlight their efficiency gaps as a magnifying glass.

Forced Upgrading Founders: VC Iron Law and AI Threshold

Surrounding the statement that "invested companies must deeply use AI or be eliminated,” currently remains in the realm of unverified information, and should not be treated as a confirmed industry consensus. However, discussing it as a potential evolutionary direction is quite representative: in a track where efficiency is the core competitive advantage, will capital gradually form rigid or semi-rigid requirements to filter projects based on their level of AI usage?

For entrepreneurs, this trend is clearly a double-edged sword. On one hand, it amplifies efficiency dividends: teams that can truly integrate AI into product design, risk control systems, and user growth can run a more complete closed loop with fewer people and shorter durations, thereby gaining higher premiums in financing and valuations; on the other hand, it can easily lead to “AI buff stacking” style hollow packaging—constantly piling up model terms and tool lists in business plans and roadshows, yet lacking any verifiable implementation paths and effect assessments. Once in execution stages, the team could be undermined by the technical fantasies they’ve created.

In the VC and project game, AI serves as both a capital-driven tool for landing and a sieve for projects to selectively filter capital. If institutions treat “AI usage” merely as a marketing label rather than providing real models, data, and engineering capabilities in post-investment management and resource support, teams with AI capabilities will instead use “can you understand and enhance our AI capabilities” to filter out potential partners. Ultimately, what truly forms closed loops are often those combinations that find balance between technical understanding, business comprehension, and capital support, rather than a one-dimensional scenario of “capital pressuring projects” or “projects passively catering to capital.”

It is crucial to recognize that AI applications in the cryptocurrency sector still encounter clear real-world boundaries. Computing power costs determine the scalability and real-time capabilities of models, data quality dictates the reliability of analysis and decision-making, compliance regulations set red lines for on-chain data use, user privacy, and cross-border transfer, while security constraints require leaving ample space for human review between smart contracts and automated operations. Under these constraints, the segments most likely to enjoy AI dividends often concentrate on:

● On-chain analysis and risk control tools: Using AI to identify abnormal fund flows, governance attacks, and protocol risks, providing “security layers” for institutions and regular users;

● Intelligent market-making and trading infrastructure: Optimizing market-making strategies, reducing slippage, and improving capital efficiency using models under compliance prerequisites;

● Developer and user toolchains: Lowering Web3 development thresholds and accelerating product trial-and-error and on-chain processes through smart contract assistants, automated testing, and deployment tools;

● Compliance and audit assistance: Helping project parties and institutions generate compliance documentation, disclose risks, and pre-review audit reports using AI in a constantly shifting regulatory environment.

In these scenarios, AI is not a standalone hype concept but an “invisible engine” embedded in existing business logic that enhances unit resource output.

Between Fear and Greed: Reserving Safety Margins for War and AI

If we piece together Yi Lihua’s recent remarks on X, a relatively clear narrative emerges: as the shadow of war suppresses valuations and raises risk premiums, the AI efficiency revolution is quietly rewriting the industry’s production methods and organizational structures. The superimposition of these two forces does not shape a linear trend but rather generates price trajectories that are bound to be filled with extreme fluctuations. In such a trajectory, deep pits and sharp rebounds coexist, and the “great bottom-fishing opportunity” is more like a harbinger of future large-scale erroneous kills, while AI stands as an endogenous variable that determines who can survive and continue to expand during downturns.

For readers, a simplified framework can be distilled: use geopolitics and liquidity to judge “when not to buy,” and use technology and efficiency to judge “what to buy long-term.” Currently, whether there exists a risk of war escalation, and whether macro liquidity is in a tightening channel, are upper constraints determining the overall exposure of a position; while whether a particular project truly possesses AI capabilities and whether it achieves efficiency leaps on product and organizational levels determine its logic for surviving valuation compression in the next cycle. The former helps you avoid being wrongfully killed by doing the right thing at the wrong time; the latter aids you in choosing those assets with long-term compounding capabilities at the right time.

Equally important is how to utilize the views of opinion leaders. Whether it’s “there will definitely be a great bottom-fishing opportunity this year” or “going from carriages directly to the era of airplanes,” these metaphors are better served as references for cycles and directions rather than mechanically translated into specific position and leverage directives. Market participants need to construct their own decision-making systems based on their understanding of the macro logic, combined with their risk tolerance, capital cycle, and cognitive boundaries, rather than seeking “standard answers” on social media.

Looking forward to the next year, if genuinely some sort of “great bottom-fishing window” occurs, the true watershed may lie not in who calls it first or who buys at the absolute bottom, but in who can complete self-upgrading of AI capabilities and organizational efficiency during the valley period. For projects and entrepreneurs, this means reconstructing products and teams with AI during the winter; for investors, it means being able to calmly judge which assets are worth traversing the next full cycle with a three-dimensional framework of “war—liquidity—technology” during times dominated by fearful sentiment.

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