📑 Note: 🙶Constructive Fill-in🙷~ Unity of Knowledge and Action
⚡🔥 A single ChatGPT exchange ≈ 0.43 Wh — do the math: what’s the total energy bill for 2025, or 2035?
🌍🤖 Years of daily of AI Questioning and Answering exchanges — how will they reshape our lives, our relationships, and our collective future?
💧⚡Imagine in 2035, with 30 billion queries a day — how will that reshape our cities and nations?
As in life itself: our natures may be alike, but our habits set us apart.
Day after day, these AI exchanges — “a lamp’s worth of power, a ladle’s worth of water” — how do they fill the gaps in our lives and rewrite our life trajectories?
Month after month, these AI interactions and transactions, measured in data and energy, how do they reshape the rules of the world and its future?
These seemingly “everyday” behaviors are, in fact, entangled with the interweaving of multiple “socio-technical systems” — including the accumulation of habits and value judgments that shape a life.
Following on from the ❝Introduction❞, this ❝Note❞ chapter invites readers to reflect together: starting from 2025, how might we foresee our 2035 selves?
Imagine how humanity might move from ❝Mental Fill-in❞ (a cognitive shortcut) toward 🙶Constructive Fill-in🙷 (deliberately filling the gaps in life and society, as needed) — and in doing so, build the future.
🔭The Value of Technological Foresight🦾
🙶Constructive Fill-in🙷 is a form of “technological foresight” — a capacity for technological forecasting.
This book is, at once, an AI toolbook aimed at promoting AI engineering and education, and a change-management manual that hopes to make the capacity and benefits of “technological foresight” no longer the exclusive privilege of experts or governments, but a cognitive capacity anyone can cultivate.
At the core of “technological foresight” are:
- 📈 推演未來：將這些訊號延伸，想像它們在 5 年、10 年後可能帶來的社會與個人影響。
- 🔭 Observing Trends: Catching the minute traces of technological development in everyday-life data, news, and even community culture.
- 🛠 行動補全：把想像轉化為具體選擇，投資未來、解決問題、發揮價值。既可能補全個人生命的缺口，也可能補全社會的需求。
為什麼要用「補全」？除了聯想到「自動補全」（auto-complete），本書假設讀者有以下應對 AI 發展的需求：
- 👤 個人層次：預見 AI 在地與全球發展，如何影響自己「生命機運」的選項與選擇。
- 👨👩👧 家庭層次：預想 AI 如何改變日常生活，如何影響自己與家人「生命交織」的關係與照護。
- 🌐 社會層次：預判 AI 如何改變社會肌理與世界格局，如何推動制度與科技的創新。
🧩💪 AI 🙶補全🙷 力
科技預見 AI 🙶補全🙷 力，對於個人自主未來（autonomy-complete），包含對 AI 的核心發問：
- ⏳🌌 時間空間尺度：意識到「一次問答 ≈ 一盞電、一瓢飲」與「每日數十億次 ≈ 一座城市、一個國家」之間的放大與加速效應，學會在微觀與宏觀之間切換視角。
- 🤼🏙 社會技術系統：運用趨勢觀察與未來推演，理解個人日常選擇如何累積成社會與國家級的基礎設施壓力，並思考「生命機運」與制度、環境永續的路徑，鍛鍊科技預見 🙶補全🙷 力。
- 💪🆚🦾 人機學習對照：系統化比較人類「腦補」的直覺捷徑與 AI 「推論」的能耗代價，反思如何在人機協作中找到最適的 🙶補全🙷 互動，確保自己與後代的未來。
🌟 科技預見 AI 🙶補全🙷 力，就是讓每個人都能用「未來視角」來補全當下 AI 學習、使用、及應用的不足。
它不是遙不可及的專業，而是人人可練的心智能力：幫助我們看見機會，把握潛能，在人生與社會的旅程中，實現「知行合一」。
🌟TLDR言簡意賅 〜 讀者可以按需操練，鍛鍊「科技預見」🙶補全🙷力：
- ⏳🌌 時間空間尺度：練習在微觀與宏觀之間切換視角。
- 🤼🏙 社會技術系統：透過「人類腦補」與「AI 推論」，鍛鍊預見力。
- 💪🆚🦾 人機學習對照：「人類腦補」與「AI 推論」互補，確保未來。
總之，把 ❝腦補❞ 力鍛鍊成 🙶補全🙷 的能力，是個體與集體人生旅程的知行合一修行。
🦾💪
準備好了嗎？在深入探討「思維定勢」與「框智力」之前，不妨先動手做一個小小的科技預見練習——見 🔖附錄A. 💪 行動 中的《AI 代價與未來生活預見練習》，親手估算一次 AI 問答的能耗代價，並想像自己在 2025、2030、2035 三個時間點的生活樣貌。
這個練習包含：
- ⚡ 一張 AI 代價預見表（能耗、耗水、資料中心用電的 2025→2035 估算）
- 🔮 五個 AI 未來假說（對齊崩潰、公共財、帝國、部落化、公用事業）待你檢驗
- 🏙 一張 AI 個人生活預見表，邀你填入自己 2025、2030、2035 的想像
完成後，你會更能體會「被補全」與「自補全」之間的抉擇。詳見 🔖附錄A. 💪 行動。
⏱️🖼️ 定勢與框架
本書自始即定位為工具書，不在於宣揚哪個世界更好、哪個目標更妙，而在於提供一套可用的工具，讓讀者在設計或使用智能系統、安排自己與智能社會的關係時，能有所依循。
❝腦補❞ 與 🙶補全🙷 之間，其實還有「思維定勢」或譯「心理定勢」（mental set）的概念。它指人類在解題或思考時，習慣使用「套路」（即過去成功的方法）來處理新問題。這能快速套用經驗、節省思考成本，但也容易導致「認知僵化」。這種取捨，正與「快思慢想」⚡🧮雙系統理論 的二元模式相呼應。
的確 ❝腦補❞ 本能是勾勒出經驗與想像「事實」，驅使我們「行動」的「知行捷徑」，這包括演化出來生下來就可能有的 完形心理，也包括後來環境及社會習慣學得的「套路」，或較符合教育或心理學的說法，「經驗法則」（heuristics）。
這些經驗法則的範圍極廣，從古代的甲骨占卜、占星術，到現代的魔術、厚黑學、甚至兩性話術，都帶有人類作為生物本能的 ❝腦補❞ 面向，伴有人類社會學習模仿的「套路」與「思維定勢」。「思維定勢」可視為「經驗法則」(heuristics) 的一種具體表現。
就連啟蒙及科學理性的前期基礎，似乎脫離不了「經驗法則」的捷徑省腦思維。（當然，如可證偽、理論建構、等等更具有打破「套路」的嚴謹科學或哲學方法等等，是想超越「快思捷想」的侷限。）
所以這本工具書的目標就能比較清楚且謙和的說明：介紹 AI 如何建構出不同的❝腦補❞套路，以及說明幾種較具科學或實踐意義的「經驗法則」，在這一點上可能和占星、魔術、厚黑、把妹等等書籍差不多。差比較多的可能是說破這些「套路」的同時，突出其本質上要應對的 AI 問題意識，這樣子就把改變及創新的責任又交還給讀者。
換句話說，這本書不像套路書籍，不蛇油化或萬金油化 AI 。（這也說明，我為什麼每次聽到某專業院長說自己的專業是「萬金油專業」時，會不舒服。）
從中文日常語言的「套路」，可以連結到許多知識的習慣，如「SOP」（強調某組織或文化已訂的標準化或制式化流程）、老梗（強調缺乏創意或了無新意）、話術（強調語言上的技巧與欺騙性）等等，這佐證了「知行合一」的初階或表面形式。
如此，讀者即可較容易理解 框架問題 為何是 AI 問題意識的核心「套路」問題，而 符碼紮根問題 為何可以看成某一「套路」在某一場景的有效性檢驗問題。
- 心理定勢：人類的「思考慣性」，會限制我們在解題時的靈活度。
- 框架問題：「資訊過載或不足」的問題，AI 必須決定哪些因素要納入推理，若不加限制，會陷入「無窮考慮」，若過度簡化，又可能忽略關鍵因素。
👉 可以這樣理解：
- 人類因為心理定勢，太快排除 其他可能解法。
- AI 因為框架問題，太難排除 無關資訊，因此常陷入無窮考慮。
這種對照其實很有啟發性：
- 若人類能學習 AI 的「全面考慮」，就能避免思維僵化，（但也容易找不到北）。
- 若 AI 能學習人類的「直覺取捨」的專注力，就能更快聚焦在關鍵因素，（但也易習得人之偏見及定勢）。
這也說明『人機協作』的價值不在於簡單拼接，而在於理解『此套路』與『彼套路』的差異，判斷能否真正互補，而非笨上加拙。
💪🦾 框智力🖼️
為了更清楚說明這本工具書的用處，我將 ❝腦補❞ 到 🙶補全🙷 的能力，提升為一種行動準備的心智能力——「框智力」（frame-setting capabilities, or simply frame intelligence）。
框智力：指在大千世局中，如何依脈絡情境，「謀定而後動」。
它呼應了第壹篇 [@#nte-problematics]中的核心議題：
-
脈絡
- 👁️⯊ ?sec-gestalt-psychology
- 🗫🎲 ?sec-language-games
- 👁️⯊ ?sec-gestalt-psychology
-
定
- 🖼️⏱️ ?sec-frame-problem
- 🔤⚓ ?sec-symbol-grounding-problem
- 🖼️⏱️ ?sec-frame-problem
-
動
- 🎯🛡️ ?sec-alignment-control-problem
「謀定」：依情境選擇「套路」或「經驗法則」。更高層次上，則是依脈絡（常伴隨時間壓力）與認知資源（無論是生物或機器）去應對 ?sec-frame-problem 與 ?sec-symbol-grounding-problem。
「後動」：則是依行動前後的目標與結果，進行對齊與控制。
因此，「謀定後動」是一種專注力，也是一種有效能行動的情境感知力——而情境感知本身必然伴隨取捨。
框智力的取捨：指「弱水有三千，只取一瓢飲」為行動準備的專注力取捨。
這源自對人類「行動力」與「學習」的觀察與假設：
-
知識🡾行動：學習本身就是行動——無論是模仿經驗，還是吸收知識，都是在準備行動或以行動作為準備。
- 🧰 將所學轉化為行動
- 🧰 將所學轉化為行動
-
行動🡽世界：學習同時也是個體在世界中展開多層次心智活動的方式。
- 🗺 讓行動成為與世界互動的橋樑
然而，世事如長河奔流、萬象紛呈——「弱水有三千，只取一瓢飲」正點出，在世之「局」中，專注力對行動的關鍵影響。
正如行旅於大千世界的旅人，需深知「弱水有三千，只取一瓢飲」——在無限可能中，選擇最契合當下的行動。
⛵ 行動與主客觀交織的歷程，如行舟於四海八荒：
- 🡼🐣 主觀經驗（內在的「我」）：體會「如人飲水，冷暖自知」
- 🡽🐥 客觀經驗（外在的「它」）：明白「弱水有三千，只取一瓢飲」，亦知「水火無情」
- 🡿🤝⚔ 主體間（內在的「我們」）：或見「水乳交融」、「水火同舟」、「水火相安」，或「水火不容」
- 🡾🏛️🌐 客體間（外在的「它們」）：見證「水火既濟」，以及「救人於水火」
框智力讓選項更清晰，為選擇做好準備。
擇其所需、取其所長，既能渡己，亦能渡人。更多詳見 🔖附錄A. 💪行動 ?imp-aqal-4-quadrants。
框智力：
- 一種由 ❝腦補❞ 走向 🙶補全🙷 的心智能力
- 依 世界 與 脈絡
- 👁️⯊ ?sec-gestalt-psychology
- 🗫🎲 ?sec-language-games
- 👁️⯊ ?sec-gestalt-psychology
- 謀定而後動
-
定
- 🖼️⏱️ ?sec-frame-problem
- 🔤⚓ ?sec-symbol-grounding-problem
- 🖼️⏱️ ?sec-frame-problem
-
動
- 🎯🛡️ ?sec-alignment-control-problem
-
定
更多詳見 🔖附錄A. 💪行動 ?imp-aqal-4-quadrants。
💡 結合「如何思考自己與世界」與「在何種脈絡下思考」，構建全景式行動框架。
🌐🦾 Scaling Frame Intelligence: Organizational Dynamic Capabilities and Cybernetic Governance
The previous section’s Frame Intelligence — “deliberating before acting” — was mostly about mental training at the individual scale. Scale the same logic up to firms, governments, and societies, and management theory and political science already offer ready-made frameworks that corroborate it — some of them predating the AI field itself.
🔭🤝🔧 Dynamic Capabilities: Frame Intelligence at Organizational Scale
Strategic management scholar David Teece’s (Teece et al. 1997; Teece 2009) theory of dynamic capabilities argues that organizations need three capacities to keep winning in volatile environments — and these map neatly onto Frame Intelligence’s “deliberate, then act”:
- 🔭 Sensing: detecting opportunities and threats in the environment. Corresponds to this book’s “technological forecasting” — observing trends and projecting futures.
- 🤝 Seizing: mobilizing resources to capture an opportunity and turn it into action. Corresponds to “action fill-in” — converting imagination into concrete choices.
- 🔧 Transforming / Reconfiguring: redesigning organizational structure and resource base. Corresponds to the dynamic re-tuning of the “deliberate” (定) half of Frame Intelligence — resetting the frame to fit the context, rather than running one framework indefinitely.
In other words, dynamic capabilities theory asks: can an organization move from a “mental-fill-in” style of inertial reaction (blindly reapplying old playbooks) to a “constructive-fill-in” style of Frame Intelligence (re-deliberating under a new context)?
By 2026, management scholarship is actively debating how AI — including agentic systems — constitutes a foundation for dynamic capabilities: new concepts like Agentic Operational Capability (Francis and Patri 2026), human–AI collaborative strategic decision-making (Karami 2026), and new cognitive architectures for senior executives (Wu et al. 2026) all point the same direction — this theoretical path is the next step in scaling individual cognition into organizational governance. By 2026, the tools facing corporate leaders are no longer simple software but AI agents with Autonomous Orchestration Capacity. Through human–AI collaboration, AI systems can push past traditional digital boundaries and dynamically adjust decision rules, reshaping senior executives’ cognitive architecture and helping resolve the perennial organizational dilemma between flexibility and stability. In other words, organizational dynamic capability has evolved from purely human judgment into a symbiotic decision network — sensing, seizing, and reconfiguring together with multiple AI agents.
🎯 Leap insight: Future competitive advantage won’t come from how much compute you stockpile, but from whether your organization can move seamlessly with its AI agents — turning every environmental shock into a springboard for re-deliberation.
🧭🤝 Cybernetic Governance: The “Steering” Metaphor for Frame Intelligence
Political scientist Karl Deutsch’s (Deutsch 1966) The Nerves of Government proposed cybernetic governance: a government or organization need not rule by coercive command — it can, like a nervous system, “steer” (the original sense of cybernetics) society by receiving, transmitting, and responding to feedback.
This connects directly to what AI engineering circles have been calling Harness Engineering since 2026: constraining agentic systems by governing computational emergence (Mittal and Rainey 2015) and steering it toward outcomes aligned with system goals.
Deutsch distinguished between Lag and Lead response modes — which turn out to be another academic vocabulary for “mental fill-in” versus “constructive fill-in”:
Lag-mode response: waiting for a problem to occur before reacting. Like “mental fill-in,” or a VLA robot with no internal model, blindly executing actions purely off a performance feedback loop.
Lead-mode response: building a model in advance to anticipate problems before they occur. Like “constructive fill-in,” or a world model — a “Will,” equipped with memory and value judgment, driving action.
🎯 Leap insight: True steering was never about pulling the reins tighter under force — it’s about building an organizational nervous system with the “lead-mode fill-in” will to turn, gracefully, before the crisis ever takes shape.
🧭📡 Systems Management: Frame Intelligence’s “Viable” Systems Model
To address the challenge of dynamic adaptation in complex organizations, management scientist Stafford Beer published his classic trilogy (Beer 1972, 1979, 1985;2007), grounding cybernetic principles fully in organizational design and proposing the Viable System Model (VSM).
Beer ingeniously used human neuroanatomy as his prototype, arguing that any system capable of independent survival has internal information flows and control nodes that are fractal and recursive in nature. He decomposed this structure — one that maintains systemic homeostasis while dynamically responding to external change — into five tightly coordinated subsystems, from the ground up:
- System 1 (Operations / muscles and organs): the basic autonomous units that interact directly with the external environment and create real value — “the doing parts.”
- System 2 (Coordination / spinal cord and parasympathetic nervous system): anti-oscillatory — prevents autonomous units from generating internal conflict through resource competition.
- System 3 (Internal control / pons and medulla): manages “here-and-now” day-to-day scheduling, optimizing current operations and allocating resources.
- System 4 (Strategic intelligence / diencephalon and cortical foresight): scans the “outside and then” — the external environment and future — capturing opportunities through simulation, foresight, and R&D.
- System 5 (Policy and core values / the cortical “president”): responsible for the system’s ultimate “closure” — defining organizational identity and the highest guiding principles (System Ethos), and striking the critical balance between System 3’s present-optimization and System 4’s future-adaptation.
This structure maps beautifully onto the micro-foundations of Frame Intelligence. System 4’s predictive, forward-scanning role is exactly “technological forecasting” at organizational scale; System 5’s self-adjustment of core values and identity precisely corresponds to the moment in Frame Intelligence when the “deliberate” (定) in “deliberate, then act” is redefined — the moment of reframing.
By 2025–2026, both practitioners and scholars had begun applying VSM’s five-layer neuro-cybernetic framework to rethink accountability and steering strategy for multi-agent systems within AI Governance. This shows that Frame Intelligence and cybernetic governance are talking about the same thing: is your system merely reacting to what’s already happened (System 3’s lag loop), or can it build a model in advance and anticipate the future (Systems 4 and 5’s lead-mode fill-in)?
🎯 Leap insight: An organization lacking System 5’s capacity to reshape its own values is, at best, an efficient but blind execution machine. Only by writing “Frame Intelligence” into its architectural DNA can an organization stay unassailably viable amid the turbulence of the multi-agent era.
🌟 TL;DR — Frame Intelligence isn’t just individual mental training. Scaled up to organizations and societies, its core comes down to:
- 🔭🤝🔧 Dynamic capabilities: sense (technological forecasting), seize (action fill-in), reconfigure (re-deliberate).
- 🧭📡 Cybernetic governance: lead-mode (🙶constructive fill-in🙷) beats lag-mode (❝mental fill-in❞) — steer by feedback, not by command.
Whether at the level of individual mind, corporate architecture, or national governance, Frame Intelligence’s ultimate question stays the same: is your system merely reacting to what’s already happened, or can it build a model in advance and anticipate the future?
🦾💪🔥💧
📌 Summary: The Evolution of Frame Intelligence, from Individual Mind to Organizational Cybernetics
From individual mental training to governance at the organizational and societal level, Frame Intelligence offers a unified architecture spanning macro and micro scales:
- Individual level: moving from blind, “mental-fill-in” inertia to context-recalibrated, “constructive-fill-in” Frame Intelligence.
- Organizational level: folding agentic AI into a dynamic-capabilities loop of sensing, seizing, and reconfiguring — a symbiotic human–machine decision network.
- Governance level: steering computational emergence through the Viable System Model and lead-mode feedback, building systems capable of self-reframing.
Armed with Frame Intelligence — grasping both the cutting edge of technical evolution (agentic AI, emergence-steering, multi-agent governance) and classic theory in management and political science (Teece’s dynamic capabilities, Deutsch’s cybernetic governance, Beer’s Viable System Model) — this book helps readers see both the forest and the trees in AI knowledge, and put it to work: Harness Intelligence. “Deliberate, then act” turns out to be not just an ancient habit of mind, but the underlying logic by which complex systems stay viable — and keep evolving — under deep uncertainty.
🪜 通向附錄的下一步
在理解了「謀定後動」的框智力後，人類 ❝腦補❞ 力與科技 🙶補全🙷 力之間，就有了比較參照的具體流程單元：先框定脈絡情境感知，後決策行動。
若你想繼續深化，這裡有兩條路：