富人在加密货币中如何越来越富?

深潮Publicado em 2025-07-29Última atualização em 2025-07-30

没有运气,没有赌博,只有经过验证的策略来在加密货币中积累持久的财富。

撰文:Crypto Unfiltered

编译:Block unicorn

前言

你不需要中彩票或挑选下一个奇迹币来在加密货币中积累财富。的确,有些人很幸运,比如以 3 美元买入比特币并提早退休的人,但依赖运气是一个糟糕的策略。

那么,富人如何在加密货币中持续、稳定地变得更富有,而不靠赌博式的模因币?

无聊的真相:买入并持有

最简单、最有效的方法也最不刺激:购买比特币、以太坊或其他顶级加密货币,并长期持有。自 2012 年以来,比特币的平均年回报率约为 150%,几乎超过所有其他资产类别,包括股票,在同一时期与比特币相比下跌近 100%。

持有听起来可能很无趣,但问问自己:你是想追逐最新热门代币的 100 倍回报(然后很可能全赔),还是随时间稳步增加财富?

避免重大错误

加密货币市场充满诱惑和轻松赚钱的承诺。大多数投资者都犯了同样的错误:

  • 过度杠杆:借钱投资超过自己能承受的损失。这从来不会有好结果。

  • 追逐高收益:去中心化金融(DeFi)中的高收益诱人,但往往伴随着隐藏的风险。问问那些在 Luna 崩盘中亏钱的人。

  • 投资集中:过度押注单一币种或炒作概念,当热潮消退时可能导致巨大损失。

富人通过避免这些陷阱保持富有。他们分散投资组合,谨慎管理风险,并明白亏钱比赚钱的痛苦更大。

如何管理你的投资组合

从简单开始:

  • 金本位成本平均法(DCA):定期投资固定金额,不论价格波动如何。这种方法能平滑波动,消除情绪化决策。

  • 明智地分散投资:将大部分资金投入比特币和以太坊等经过验证的资产,少量分配给有潜力的其他币种。

  • 保持警惕:安全至关重要。使用硬件钱包,不要点击可疑链接,始终审慎选择存储加密货币的地方。

长远规划与耐心

在加密货币中最难的不是选对币种,而是在市场剧烈波动时保持耐心和情绪上的超然。真正的加密财富不是一夜之间创造的;它在市场周期中逐渐累积。早期购买比特币的人没有在第一次 100% 涨幅时套现。他们懂得耐心的力量。

加密货币的超能力:行为激励

与股票或债券不同,加密资产带有内置激励机制(代币奖励、社区治理等),保持用户参与和忠诚。这种独特属性确保了长期用户增长和网络效应,最终推高价值。

享受生活,保持积极

积累财富不应意味着牺牲幸福或生活质量。加密货币市场可能充满压力,时刻监控价格波动会让你精疲力尽。享受你的爱好,与家人朋友共度美好时光,拥抱金融之外的体验。

通过专注于你能控制的事情保持积极:你的行动、你的学习和你的成长。培养耐心,记住真正的、重要的成功需要时间。活在当下,庆祝你的进步,不要让市场波动主宰你的幸福。

最后一点思考

如果你只记住一件事,那就是:加密货币仍处于早期阶段。目前整个加密市场估值约为 4 万亿美元,未来十年可能增长到 50-100 万亿美元。

在加密货币中积累财富不需要运气或内幕消息。你只需要一个明智的方法、耐心和坚持的纪律。

或许,还要抵制卖房去买下一个模因币的冲动。

Leituras Relacionadas

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbitHá 1h

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbitHá 1h

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbitHá 1h

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbitHá 1h

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbitHá 2h

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbitHá 2h

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbitHá 2h

Token Inefficient, Economy Tokenless

marsbitHá 2h

Trading

Spot
Futuros
活动图片