人工智能同區塊鏈科技喺2025年迅速融合，開創多個行業嶄新模式，有望重塑數碼經濟。兩者結合咗AI嘅運算力同Web3去中心化框架，一方面解決到各自不足，同時帶嚟創新新機遇。資產管理公司Bitwise 預測，AI同加密貨幣結合，有機會到2030年為全球GDP帶來高達20萬億美元增長，彰顯出業界對此融合嘅龐大期望。
必須知道嘅重點：
- 由2022年開始，AI投資大幅增長，而家有42%美國創投資金流向AI企業，兩年前只係22%。
- Web3，即用區塊鏈構建嘅第三代互聯網科技，透過公開、不可更改紀錄，為AI「黑盒」問題提供解決方案。
- 雖然數據私隱同管治仍面臨重大挑戰，業界預期AI同加密貨幣融合，到2030年有望為全球GDP增加20萬億美元。
Web3演變史
先同大家搞清基本概念。
Web3係互聯網第三代技術，強調去中心化及用戶擁有權，建基於區塊鏈，明顯有別於早期網絡。90年代嘅Web1.0要靠靜態資訊網站；去到2000-2010年代變做Web2.0，引入咗互動同社交網絡，但到頭來又變返由大型科技公司控制數據。
「Web3」一詞由Ethereum聯合創辦人Gavin Wood於2014年提出，21年加密熱潮時起開始大行其道。佢依賴開源區塊鏈網絡代替企業伺服器，加密代幣使數碼資產持有人同社群都可以參與管治，毋須信任中介人或傳統監管機構。
Web3關鍵技術包括比特幣及以太幣用作P2P支付，另有智能合約，自動執行鏈上協議。Ethereum 2015年推出後，開放智能合約更多用途，包括去中心化金融（DeFi）協議、數位所有權NFT，以至DAO社群管治。
2021年NFT作品賣出天價，加上Facebook改名Meta，掀起Web3初期熱潮。不過2022年市場回調後，炒作冷卻，業界變得更現實。期間主鏈升級、新興區塊鏈冒起，同時Layer 2網絡提升交易效率，基建日趨成熟。
去到2025年，生態圈大幅成長。大家明白去中心化帶嚟創新商業模式同創意，但用戶體驗、管治同安全仲有好多改善空間。喺區塊鏈上儲存大量價值同數據，市場開始需要智能化工具協助善用—AI正好切合。
ChatGPT後AI急速變革
AI成為高科技界新霸主。
ChatGPT於2022年底面世，標誌住AI史上一次大變革，對業界衝擊猶如iPhone對手機界一樣。兩年間，由小眾技術演變為企業創新核心。
到2024年初，超過75%受訪企業有採用AI於至少一個業務範疇，65%經常用生成式AI—比上一年幾乎翻倍。
爆炸性應用增長全靠技術大躍進。例如OpenAI GPT-4大幅提升生成內容水準，Google同Anthropic等對手亦殺入市場。硬件需求爆升，NVIDIA顯示卡一度令市值衝上萬億美元。
非科技傳統行業同樣引入AI方案。金融機構用AI偵測詐騙同管理投資組合，製造商靠AI機械人與預測維護，傳媒以AI針對性推薦內容，政府亦用AI提升公共服務效率。雲端平台廣泛普及，企業可以透過API或租用伺服器微調AI模型。
迅速擴展帶來倫理、私隱及可靠性疑慮。偏見算法或AI失控事故屢登新聞，推動監管加強。歐盟推行AI法案，有地區甚至暫停部分AI應用以防私隱風險。
AI與Web3互補優勢
2025年AI同Web3融合，兩種技術表面一左一右，實際產生強大協同效應。AI大幅提升去中心化應用嘅可用性同智能。過去區塊鏈用戶體驗好爛、能做嘅事有限，AI接入後，智能合約變得更聰明，可以提供即時反應服務。
AI可以分析即時數據，根據複雜情境啟動智能合約—例如DeFi借貸自動因應市場或用戶信貸條件作出調整。AI介面亦會引導新手了解應用，令交易同互動更人性化，加快主流採納率。
向相反方向，Web3又為AI補足短板。區塊鏈透明度解決AI「黑盒」難題，將訓練數據、參數同決策全記錄落不可篡改帳冊，達到可查核標準。監管及用戶都檢視得到某AI如何訓練同運作。區塊鏈身份系統仲可以幫AI持牌作事，建立可靠數碼身份，尤其AI將來會幫人自動交易。
Web3式數據擁有權更開創新思路。不再係科技公司瓜分數據，而係用戶自主貢獻訓練AI數據，仲可以收取代幣報酬，保有權益。
雖然潛力巨大，現實重大挑戰依然存在。AI需要大量數據，但區塊鏈本身極度透明，引發私隱疑慮。聯合學習、零知識證明等技術或可在不洩露私隱下運行AI，但仲係發展中。數據一經上鏈無法刪除，落實GDPR等監管法律亦變得複雜。
AI於Web3真實應用場景
金融服務轉型
去中心化金融（DeFi）係AI-Web3融合最具潛力範疇之一。到2025年，AI令DeFi更聰明更普及：可以自動評核信貸風險、優化收益組合、執行自動交易。
Robo-advisor全天候監控加密市場，根據客戶風險喜好自動調整資產組合。這類AI智能理財機械人，就如微型對沖基金一樣喺鏈上運作，為小額投資者開放高級金融策略。
區塊鏈支付同樣受惠AI。穩定幣（與法定貨幣掛鈎嘅加密貨幣）流通量由2020年4十億美元升至2024年近2千億。AI結合穩定幣網絡，可自動執行複雜金融操作。企業以AI分析市場主動調整資金流動，滿足營運所需，觸發付款或對沖指令。日常流程更智能高效，定時自動完成，減低人為錯誤。
AI更推動Web3全新金融產品，如參數保險：當觸發指定情況時自動賠償，由AI即時分析鏈外數據。這令微型保險普及，例如為農民設計經濟實惠氣候險，當AI偵測干旱時自動發穩定幣賠款，毋須繁複文件流程。
現實案例：
AI結合去中心化金融平台（如Circle USDC穩定幣），自動化金融操作，包括即時AI穩定幣交易和智能組合管理。Aave、MakerDAO等項目亦運用AI強化借貸、交易同風險管理。
去中心化管治新進化
DAO（去中心化自治組織）利用AI協助協調同提升決策效率。傳統DAO有時討論氾濫，幾千個成員亂講亂投。AI會先分析社交網絡整體氣氛，正式投票前自動摘要長談，令討論重點清晰...
reducing participation barriers.
降低參與門檻。
AI agents themselves are becoming participants in DAO ecosystems. Experiments include AI agents receiving grants to develop investment strategies, essentially functioning as fund managers under DAO oversight. In other cases, bots handle routine tasks like treasury rebalancing or community moderation according to guidelines established by human members.
AI代理本身亦成為DAO生態系統嘅參與者。有啲實驗包括畀AI代理資助去設計投資策略，本質上喺DAO監督下充當基金經理。喺其他情況下，機械人會根據人類成員制定嘅指引處理日常事務，好似資金庫重新平衡或者社群管理咁。
Treasury management represents a concrete application where AI demonstrates value. Many DAOs control significant funds, sometimes exceeding $100 million in crypto assets. AI-based portfolio management tools can automatically diversify assets or generate yield through DeFi protocols while adhering to community-defined risk parameters.
資金庫管理係AI展現價值嘅一個實在應用。好多DAO掌握住大量資金，有時甚至擁有超過一億美元嘅加密資產。基於AI嘅投資組合管理工具可以自動分散資產或透過DeFi協議賺取收益，同時符合社群訂立嘅風險參數。
These agents follow encoded rules with all transactions logged on-chain, providing complete transparency.
呢啲代理係按照編碼規則運作，所有交易都會記錄喺鏈上，實現完全透明。
Organizations approach AI integration cautiously, typically keeping humans in supervisory roles. Trust develops by allowing AI to execute strategies while humans retain policy-setting authority and override capabilities. Web3's transparency makes AI actions traceable in ways traditional corporate AI often isn't—every on-chain action by a DAO's AI can be audited by members in real-time.
組織小心翼翼咁去整合AI，一般都會保留人類喺監督角色。信任建立嘅方法係容許AI執行策略，但人類就保留制定政策同埋介入嘅權力。Web3嘅透明度令AI操作可以追蹤到，呢點傳統企業AI做唔到 —— DAO內AI嘅每個鏈上行動，成員都可以實時審核。
In the real world:
現實例子：
Decentralized Autonomous Organizations (DAOs), like Aragon and Compound, are increasingly employing AI tools for treasury management, governance analytics, and community moderation. Notably, DAOstack has experimented with AI-driven sentiment analysis and automated decision-making to streamline governance processes and improve organizational efficiency.
去中心化自治組織（DAO），例如Aragon同Compound，愈嚟愈多會用AI工具來做資金庫管理、治理分析同埋社群管理。DAOstack例如試過用AI情緒分析同自動決策，令治理流程更順暢同提升組織效率。
Creative Economy Innovations
創意經濟創新
The creative economy built around Web3 is undergoing transformation through AI integration. Artists and developers increasingly use AI tools to generate content that is owned, traded, or experienced on blockchain networks. This spans visual art, profile-picture collections, music, literary works, and metaverse environments.
圍繞Web3興起嘅創意經濟正因為AI加持而轉型。藝術家同開發者愈來愈多用AI工具去創作可以被擁有、買賣或者體驗嘅區塊鏈內容，範疇涵蓋視覺藝術、頭像系列、音樂、文學作品甚至元宇宙場景。
Generative art NFTs represent a notable manifestation. Artists train AI models on specific styles or concepts, producing endless variations that can be minted as unique tokens.
生成藝術NFT就係個明顯例子。藝術家用某啲特定風格或者概念訓練AI模型，再製作出無窮變化、可以鑄造成獨特代幣嘅作品。
Major auction houses have validated this trend, with Christie's holding its first auction dedicated to AI-generated art in early 2025, achieving over $700,000 in sales despite mixed results.
大型拍賣行都肯定咗呢個趨勢，Christie’s 喺2025年初搞咗第一場專門針對AI創作藝術品嘅拍賣會，雖然戰績有好有壞，但總銷售額都超過70萬美元。
Interactive NFTs are emerging with embedded AI functionality. Examples include virtual pets or avatars with AI personalities that owners can interact with, evolving over time. This makes NFTs dynamic experiences rather than static collectibles. Web3 games similarly incorporate AI to create more realistic non-player characters capable of improvising dialogue and adapting to player actions.
新一代NFT帶有內置AI功能，好似虛擬寵物或者具備AI性格嘅分身，主人可以互動，而且會隨時間進化。呢種NFT唔再只係靜態收藏品，而係有動態體驗。Web3遊戲方面，AI亦用嚟製作更逼真、懂即興回應玩家行動嘅非玩家角色。
AI-generated content marketplaces are developing on Web3 platforms, allowing creators to mint AI-generated music as NFTs with automatic royalty distribution to both model creators and musicians. Some DAOs commission AI models to generate intellectual property that community members collectively manage and license across media formats, with revenue shared through tokens.
Web3平台上開始有AI生成內容嘅市場，創作者可以鑄造AI音樂NFT，系統自動將版稅分發比模型開發者同音樂人。有啲DAO就委託AI模型生成智產，交由社群集體管理，分散授權去唔同媒介，再以代幣分享收益。
The boundaries between creator, tool, and owner are blurring in fascinating ways. Web3 can record contributions to creative works and use smart contracts to allocate appropriate revenue shares. This potentially addresses controversies around AI art by automatically compensating artists whose styles influence AI outputs.
創作者、工具同擁有者之間嘅界線愈嚟愈模糊。Web3可以記錄每個人對作品嘅貢獻，用智能合約自動分配收益。甚至有機會解決AI藝術爭議，自動補償啲對AI作品有影響力嘅原創藝術家。
In the real world:
現實例子：
AI-generated art is making waves in the NFT market, highlighted by Christie’s first dedicated AI art auction featuring artists like Refik Anadol and platforms like Art Blocks. Interactive NFT projects, including Altered State Machine (ASM), are embedding AI into NFTs, allowing dynamic interactions and evolving digital collectibles.
AI藝術作品喺NFT市場掀起浪潮，Christie’s舉辦首個AI藝術品拍賣會都有Refik Anadol等同Art Blocks平台加入。Altered State Machine（ASM）等互動NFT專案已經將AI嵌入NFT，令收藏品可互動並隨時間進化。
Gaming Ecosystem Advancement
遊戲生態進步
Web3 gaming is experiencing significant enhancement through AI integration, with improvements both within game worlds and in development processes. Inside games, AI powers non-player characters and content generation, creating richer experiences. Characters in blockchain games can remember player interactions and evolve over time, with memories stored as data attached to NFTs, creating personalized gameplay narratives.
Web3遊戲因結合AI而有突破，無論遊戲世界內部定開發過程都大有改善。遊戲內，AI用於非玩家角色同內容生成，帶嚟更豐富體驗。區塊鏈遊戲入面啲角色可以記住玩家互動，隨時間進化，而且記憶會儲喺NFT數據度，做到個人化遊戲故事。
Game studios utilize generative AI for procedural content creation, rapidly producing diverse landscapes, items, and dialogue. Industry-standard game engines now include built-in AI tools for generating textures and simulating physics, helping Web3 games achieve visual and narrative depth comparable to mainstream titles.
遊戲工作室用生成式AI去自動創作地圖、物件、對白等內容，一下就可以生產大量多樣化素材。業界標準遊戲引擎而家都有內置AI工具，幫手創建貼圖、模擬物理效果，推高Web3遊戲視覺同劇情深度，接近主流大作水準。
AI is dramatically reducing development time and costs for blockchain games. According to industry leaders, AI-assisted development—generating code snippets, designing artwork, testing for bugs—has cut production timelines by approximately 65% over the past year. This enables smaller studios to compete effectively by using AI for labor-intensive tasks like character animation or economy balancing. One mobile developer described using AI to simulate thousands of player strategies overnight to optimize token reward systems, work that would traditionally require extensive testing teams.
AI令區塊鏈遊戲開發時間成本大減。業界領袖話，AI協助開發（如自動寫程式、畫圖、測試bug），一年嚟生產週期可以縮短約65%。細規模工作室借助AI應對角色動畫、經濟系統平衡等工作，就能有效同大公司競爭。有手機開發者分享，佢用AI一晚模擬數千種玩家策略去優化區塊獎勵機制，以前要測試團隊花大量時間先做到。
AI is also improving economic systems within play-to-earn games. Balancing economies where players earn real value presents complex challenges—AI modeling predicts how virtual economies respond to changes by analyzing player data, helping designers maintain stability.
AI亦可優化邊玩邊賺類遊戲嘅經濟系統。點樣平衡玩家能獲得真實價值經濟好複雜—AI模型通過分析玩家數據預測經濟反應，有助設計師保持穩定。
AI can personalize financial experiences, offering newer players accessible quests with reasonable rewards while directing veterans toward community events that sustain engagement.
AI亦可以個人化財務體驗，好似為新手設計容易入手同合理回報嘅任務，而資深玩家就誘導參與社群活動，維繫活躍度。
In the real world:
現實例子：
Web3 gaming platforms such as Illuvium and Immutable are leveraging AI to enhance gameplay with adaptive NPCs and procedurally generated content. Axie Infinity and upcoming blockchain-based games from studios using Unreal Engine 5 integrate advanced AI tools for richer, more personalized player experiences.
平台如Illuvium同Immutable正用AI來提升遊戲體驗，例如adaptive NPC同自動生成內容。Axie Infinity同啲用Unreal Engine 5製作新一代區塊鏈遊戲工作室，都加入進階AI工具，打造更個人化同豐富嘅玩家經歷。
Infrastructure and Security Developments
基建同安全新發展
Behind-the-scenes infrastructure represents a foundational area where AI and Web3 are converging. This includes enhancing blockchain networks and using Web3 principles to decentralize AI development itself. Computing power illustrates this synergy. AI model training requires immense computational resources, traditionally limited to major tech companies. Meanwhile, cryptocurrency mining has created globally distributed high-powered computer networks that are often underutilized.
台底基建就係AI同Web3交匯最基礎一環，包括加強區塊鏈網絡、用Web3原則去分散AI本身發展。算力需求好好表現出兩者配合。AI模型訓練要極大量運算力，一向只屬大科技公司專有。不過，加密貨幣挖礦帶動全球分散式高效電腦網絡，而呢啲網絡好多時未被善用。
Decentralized compute marketplaces have emerged to bridge this gap. Networks allow crypto miners and data centers to rent excess GPU capacity to AI researchers, with blockchain-based systems handling payments. This creates distributed "supercomputers" without reliance on single providers, aligning with Web3's anti-monopoly philosophy while offering miners alternative revenue streams.
有啲去中心化運算市場就出現咗，彌補呢個缺口。網絡容許礦工、數據中心出租多出嚟嘅GPU算力比AI研究人員，付款透過區塊鏈處理。咁就形成咗無需依賴單一供應商嘅分散「超級電腦」，又貼合Web3反壟斷主張，同時為礦工開拓新收入。
Similar decentralization is occurring with datasets. Web3 data marketplaces enable providers to sell access to datasets for AI training, with all transactions recorded on blockchain. This creates auditable trails showing which data trained specific AI models, addressing transparency concerns. Several organizations are exploring "model provenance" on blockchain, where each AI model update is recorded like a software repository commit.
數據集同樣經歷去中心化。Web3數據市場允許數據供應者賣數據集畀AI訓練，所有交易都記錄喺區塊鏈，清楚交代每個AI模型用咗咩數據，有助解決透明度問題。有幾間機構研究緊喺區塊鏈上做「模型來源追溯」，好似軟件庫commit記錄咁，將AI模型每次更新都記低。
Security within crypto infrastructure benefits from AI deployment. The anonymous, irreversible nature of blockchain transactions has attracted fraudulent activity that traditional monitoring struggles to detect. Exchanges and protocols employ machine learning models to analyze transactions in real-time, flagging anomalies and suspicious patterns. These systems can identify potential account compromises or prevent attacks like flash loans by simulating transaction impacts before execution.
加密基建方面，AI部署有助於安全保障。區塊鏈交易匿名又不可逆，吸引唔少詐騙，傳統監控難以偵測。交易所同協議利用機器學習即時分析交易，標記異常或者可疑圖案。系統甚至可預演交易影響，提前識別賬戶風險或者阻止閃電貸等攻擊。
Blockchain is similarly securing AI systems. As models become valuable intellectual property, verifying their integrity becomes crucial. Blockchain can timestamp and hash model parameters, effectively creating tamper-evident fingerprints. This has spawned experimental "AI model NFTs" representing ownership of specific AI versions, potentially including smart contracts that automatically compensate original creators through royalties.
區塊鏈同樣保障住AI系統安全。隨住AI模型成為重要智產，驗證其完整性至關重要。區塊鏈可用時間戳及hash值記錄模型參數，變相做到不能被篡改嘅指紋。亦已經有「AI模型NFT」實驗，代表某一版本AI所有權，甚至包括智能合約自動畀原創開發者取得版稅。
In the real world:
現實例子：
Projects like Render Network, Bittensor, and Ocean Protocol exemplify decentralized marketplaces providing GPU computing power and AI data-sharing services on blockchain. Meanwhile, exchanges including Binance employ machine learning to bolster blockchain security, fraud detection, and infrastructure resilience, enhancing user protection across crypto ecosystems.
Render Network、Bittensor同Ocean Protocol等專案展現咗去中心化市場，為AI提供基於區塊鏈嘅GPU算力同共享數據服務。交易所如Binance正用機器學習加強區塊鏈安全、詐騙偵測及基建抗壓能力，保障用戶喺加密生態圈嘅安全。
The Future of AI-Web3 Convergence
AI與Web3融合未來展望
As the AI-Web3 intersection progresses through 2025, early hype is transitioning toward practical implementation. The use cases examined demonstrate tangible progress across finance, governance, creativity, gaming, and infrastructure.
隨著AI同Web3交匯發展至2025，早期炒作逐漸轉化為實際落地。以上各種應用案例已經體現出金融、治理、創意、遊戲同基建等範疇都有實在進展。
Institutional involvement is shaping developmental trajectories. Financial organizations initially cautious about both technologies are exploring combined applications for longstanding problems. Consulting firms advise clients on integrated strategies for supply chains and identity management. Even governments are utilizing blockchain to secure public data for AI analysis. Regulatory approaches are becoming more holistic, recognizing that AI-Web3 applications span multiple domains simultaneously.
機構層面開始主導發展方向。金融機構一開始雖然對兩類科技都觀望，但宜家都試緊應用組合技術解決長期難題。顧問公司建議客戶整合供應鏈、身份管理等方案。有啲政府甚至用區塊鏈去保障AI分析嘅公開數據。監管策略亦愈趨整全，意識到AI-Web3跨越多個範疇應用。
Industry standards and collaborations are emerging at this intersection. Technical communities that historically operated separately are increasingly combining expertise, with interdisciplinary research exploring topics like blockchain incentives for federated learning or AI-optimized consensus algorithms.
業界標準同合作開始出現。過去各自為政嘅技術圈子而家多咗跨界互補，跨領域研究涵蓋協作式學習區塊鏈動力、AI最佳化共識演算法等新課題。
Looking ahead 3-5 years, several scenarios appear plausible. Consumer applications combining Web3 and AI might achieve mainstream adoption,
展望未來3至5年，好多新可能會成真。結合Web3同AI嘅消費者應用，有機會成為主流。perhaps as personal assistants managing digital assets and identity while preserving data ownership. Enterprise adoption could see significant portions of global supply chains tracked on blockchain and optimized by AI systems. Financial infrastructure might blend central bank digital currencies with decentralized finance through AI integration.
或許可以作為個人助理，協助管理數碼資產同身份，同時保障數據擁有權。企業採用相關技術後，可能會有大量全球供應鏈被區塊鏈追蹤，並由人工智能系統優化。金融基礎設施亦有可能經由人工智能整合，將央行數碼貨幣同去中心化金融結合起來。