川普為機器智能正名，開AI升格為AM之窄門

錢 宏（Archer Hong Qian）

2026年9月22日，川普先生在第81屆聯合國大會講話中提出，將 Artificial Intelligence（AI）稱為 Super Intelligence（SI）。這個提法抓住了一點：機器智能由人製造，卻並非因此沒有真實能力；它在若干領域已經強大到可以超過人。白宮公布的講話摘錄確認了這一稱呼。www.whitehouse.gov

川普為機器智能的真實性和強大能力正名，是有意義的一步。《將AI升格為AM》進一步回答：超級智能如何進入心智、生命與組織信託的關係，使“更聰明”變為“更有益於生命”。所謂組織信託，是組織因生命的託付取得能力和權力，並須向受影響的生命說明、糾錯和承擔後果。

這兩步可以前後對接，卻不能省去中間的路。AI—SI（ASI）的能力依託 CPU—GPU—TPU 等處理器發展起來；它嘗到的甜頭越多，人類越願意加速投入。但能力增長不會自行消除兩種焦慮，也不會自行突破三大瓶頸。兩者還有共同的哲學根源：人習慣以主客二元對立的眼光，把生命、技術和組織看作支配者與被支配者。

作為剛剛出版的《將AI升格為AM》一書的作者，我感到：從 SI 走向 Double AM（Artificial Mind & Amorsophia MindsField / Network），需要經過一道更難走、也更值得走的窄門。

這裡的 Artificial Mind（人藝心智）是待研究、待驗證的升格方向；Amorsophia MindsField / Network（愛之智慧信態場／網）指不同心智在具體關係中交互、反饋和生成的場與網。

二者須共同接受生命目的與組織信託的檢驗，我把種檢驗叫做AM“獎抑通機制”。

一、假花常艷卻非生命

Artificial Intelligence 這個名稱寫入1955年提出的達特茅斯夏季研究計劃，並隨着1956年的研究活動開啟了一個新領域。最初的設想是：學習和智能的特徵，能否被足夠精確地描述，使機器能夠加以模擬。它提出的是一個研究問題，並未宣告機器已擁有人類全部的思考、意識與心智。www-formal.stanford.edu

我一直主張將 Artificial Intelligence 理解為“人藝智能”。“人藝”說明這種智能出自人的知識、技藝和製造能力。它相對於自然生命而言是人造的，但“人造”“模擬”與“有沒有能力”是兩回事。

川普從 artificial 一詞聽出了“假的”意味，並沒有聽錯。假花確實是假花：它可以有逼真的花瓣與顏色，卻沒有花的生命，不會自行生長、代謝和繁衍。機器能夠模擬人的某些智能活動，也不能僅憑模擬逼真，就被認定具有人一樣的生命。

但假花的形狀和顏色真實存在。機器完成的計算、識別、推理和語言任務，也可能真實有效，甚至在特定領域遠超個人。與此同時，AI 還可能造出假的內容，例如虛構的事實、圖像、聲音和引文。因此，討論 AI 時必須分別問：它的能力是否真實？它產生的內容是否真實？它是否具有自然生命的 Mind？這裡的 Mind（心智）所指，不只是可測的解題能力，還涉及辨識、潛意識、生命經驗和愛。

“人藝智能”說明來源和生成方式；“超級智能”指出某些能力達到的程度。業界也討論 ASI（Artificial Superintelligence，人工超級智能）；本文沿用川普提出的 SI 稱呼，不讓名稱替代能力與關係的辨識。

二、兩種焦慮，一種自豪

《將AI升格為AM》所面對的第一種焦慮，是生命焦慮：AI 越強，人越擔心工作、判斷、自由和生活受到威脅；當數據、算法和組織權力結合，機器還可能成為更細密的觀察、誘導與控制工具。

第二種是競爭焦慮：個人害怕落後於同伴，企業害怕輸給同行，國家害怕現實中的競爭對手先掌握更強的 AI。於是，人一邊擔心它跑得太快，一邊又害怕自己追不上。

川普提出 SI，同時強調美國的技術領先。把兩者連起來看，可以理解這種政治心理：既然機器智能是真的、強大的，就不能讓競爭對手先掌握它；而美國若處於領先位置，又會產生展示國家技術實力的自豪。“兩種焦慮，一種自豪”是對這一處境的分析，並非說川普在講話中逐項使用了這些詞。www.whitehouse.gov

領先能夠緩解競爭焦慮，也足以令人自豪。可是，領先不會自動解除生命焦慮。一國跑在前面，仍須回答強大的智能會怎樣進入每個人的生活；每家公司都在加速，也不等於所有人都因此活得更好。當人把技術當作可無限驅使的客體（Object），又怕自己反過來成為技術和組織支配的客體，兩種焦慮便在同一關係中相互強化。

三、SI 將怎樣影響世界 AI 業界？

一次講話還不能使 SI 成為世界業界共同採用的名稱。但這個提法來自美國總統，又與領先競爭相連，可能在四個層面產生影響。

第一，改變公眾的直覺。機器智能不再容易被一句“不過是假的”打發。人們會更認真地承認它在某些任務上的真實能力，同時要求說清：究竟哪些能力超過了人，哪些仍未得到證明？

第二，改變投資與競爭的重心。“超級”會吸引資金、人才和計算資源，推動模型向更強表現邁進，也可能使規模、速度和排名變成壓倒其他問題的目標。基準成績須註明測試任務和失效邊界；排行榜靠前，並不等於系統在複雜生活中全面可靠。斯坦福《2026 AI Index》指出，部分常用評測存在可靠性及被針對性優化的問題。hai.stanford.edu

第三，抬高產品承諾。若企業稱其系統為 SI，用戶可能更願意把判斷交給它。系統越被認為“超級”，越須說明由誰設定目標、誰有權質疑結果、誰承擔錯誤。醫療、教育、金融、企業管理和公共治理中的使用，會把這種責任直接帶入人與組織的關係。

第四，推動評價標準之爭。可靠性、安全、透明、可解釋性、隱私與公平，已經是業界評價 AI 的重要方向。airc.nist.gov SI 可以使這些要求更加迫切，也可能使能力競賽跑在責任評價之前。

《將AI升格為AM》由此繼續追問：系統完成任務之後，生命得到了什麼，承受了什麼？組織是否履行了受託責任？

川普的正名，可以幫助他本人、他周圍的 AI 人士及更廣泛的業界進入這場討論。要讓這道門通向 AM，必須先充分承認 SI 的力量，再辨明力量的邊界與方向。

四、摸到了力量，就認識了“大象”嗎？

2013年，我在《哲學筆記之四：略評〈牛津共識〉》中討論過一位朋友引用的“盲人摸象”故事。摸到象腿的人說它像柱子，摸到肚子的人說像牆，摸到耳朵的人說像扇子，摸到鼻子的人說像管子。朋友認為，交換感知，再把這些描述綜合起來，便能認識大象。

交換觀察當然有價值。我的追問是：柱子、牆、扇子和管子的綜合體，真是那頭活着的大象嗎？即使換成明眼人全景觀看，用影像記錄，甚至精確解剖，也仍須認識它怎樣作為活體感知、行動、維繫自身，並與環境發生關係。“摸”“拼”“解”各有所獲；把所得相加，卻不能代替對活性機能與生成關係的認識。

今天，人類認識 AI—SI，也多少處於這樣的狀態。有人摸到語言能力，說它會像人一樣思考；有人摸到計算與謀略，說它已全面超過人；有人摸到虛假生成、就業替代或組織控制，看到的是威脅。川普提出 SI，尤其讓人注意到它力大無比的一面。這一面真實存在，值得正名；但各種能力成績與風險清單加在一起，仍不能回答 AI—SI 與人的 Mind 是什麼關係。

大象是活體；AI—SI 是否具有生命與心智，恰恰是待辨識的問題。借用這個故事，是為了提醒我們：承認每一項真實發現，還要繼續研究它們所依託的機制、交互的關係和造成的後果。把對象拆成可測部件，再將描述相加，仍難以認識關係中的生成；這正是主客二元認知需要反省的地方。

五、CPU—GPU—TPU：SI 的計算心臟

AI 的超強能力從何而來？必須把處理器放回論證的中心。

CPU 承擔通用計算與控制；GPU 的並行計算支撐大量模型訓練和運行；TPU 等專用處理器進一步針對機器學習運算進行優化。數據、算法和神經網絡依託這些架構，才能處理越來越大規模的任務，表現出越來越強的能力。CPU—GPU—TPU 是 AI 走向 SI 的計算心臟。

它們各有功能，在現實系統中共同工作，並非後一種簡單淘汰前一種。沿這條技術路線繼續改進，機器在語言、計算、識別和謀略推演等領域，還可能取得更強的超越性。

然而，計算處理器性能提高，尚不等於它們已經成為 MPU（Minds Processing Unit，心智處理單元）。MPU 是《將AI升格為AM》提出的開放研究類別：讓處理不再只面對給定數據與任務，也開始面對處境、關係、反饋與心智辨識。它不是一枚已經完成的芯片。

現行架構能夠更快地處理數據、權重和任務，卻不能僅憑這些成績，證明機器已具有人的意識，或已能夠處理 Mind、Minds 的交互與 MindsField 的生成。從計算處理進入心智處理，是一個需要重新提出並驗證的問題。

六、SI 越強，三大瓶頸越不能略過

我在近期文章中將 AI 的三大瓶頸修訂表述為：

第一，能效與能耗不匹配。單次運算可以更有效率，但模型規模、調用頻率和應用範圍也在擴大。局部效率提高，不能自動證明能源、用水及社會資源的總負載下降，更不能證明生命收益與負載相稱。

第二，系統思維的局限。AI 能在給定目標內高效處理信息，卻未必能辨明目標由誰設定、信源—信道—信果怎樣相互影響，以及系統之外誰承擔代價。一個局部看來“最優”的決定，可能把成本轉移給家庭、社區或其他組織。

第三，數據＋算法＋算力＋神經網絡≠心智（Mind），更≠愛之智慧（Amorsophia）。機器可以擁有強大的 Intelligence（智能），卻不能僅憑語言、解題和謀略表現，就被認定已具有包含潛意識、生命經驗與愛的 Mind，更不能直接等同於 Amorsophia。

三大瓶頸不會因 AI 改稱 SI 而消失，也不會隨 CPU—GPU—TPU 的下一次性能提升自行消失。競爭者可能跑得更快，生命也可能受到更強系統的反噬。“誰領先”與“領先之後怎樣生活”，必須同時回答。若不追問兩種焦慮和三大瓶頸所共有的認識前提，改名、競速與局部修補便會反覆回到原處。

七、從主客二元對立到凡事交互主體共生

近代技術習慣把人置於“主體”的一端，把自然、機器、數據乃至他人的生活置於“客體”（Subject-Object）的一端：主體設定目標，客體供其觀察、計算和利用。AI 把這種能力推向新的高度，卻也使關係倒轉：平台和算法越來越能觀察、分類、預測人，人又可能成為被處理的數據對象。把雙方簡單寫成“人控制機器”或“機器控制人”，都仍困在同一套支配邏輯里。

兩種焦慮由此發生：擔心被 AI—SI 和掌握它的組織對象化，是生命焦慮；擔心自己失去支配技術的優勢、反被競爭者壓倒，是競爭焦慮。兩者分別指向生活安全和現實競爭，卻都以“誰占據主體位置、誰淪為客體”為潛在尺度。領先帶來的自豪可以暫時回答後一種焦慮，卻無法單獨回答前一種。

三大瓶頸也顯露同一認知局限。把能源與社區當作計算之外的資源客體，容易只計算局部效率而漏掉整體負載；把目標既定的系統當作完整世界，容易看不見系統外受影響的生命和組織；把人的心智分解為數據、算法、算力與神經網絡，則容易誤以為部件相加就能自然生成智慧和愛之智慧。每一步都取得了真成果，但成果尚須進入它與生命的關係，才能辨明自身邊界。

針對數位—量子時代，人類亟需樹立“凡事交互主體共生”（Everything Intersubjective Symbiosism，EIS）的世界觀。這裡的“數位”重在主體及其處境的準確定位，“量子”提示關聯、交互與動態變化；這是一種時代的哲學命名，而非聲稱現有 AI 已經依賴量子處理器。“凡事”指向事件、心智、物質及其關係；“交互主體”要求辨明彼此不同的主體、能力與責任；“共生”立足生命自組織連接與動態平衡，使主體在有邊界的交互中生成第三種可能。生命具有不可替代的心智與目的；AI 雖由人創製，進入現實關係後也參與交互，不能只被當作被動客體；組織因生命託付而承擔責任。三者的主體方式不同，須在連接中辨明。

這不是一句附在技術末尾的口號。它改變研究的提問方式：先問誰與誰發生關係、誰設定目標、誰承擔資源負載與行動後果，再問如何設計處理架構、如何衡量效能、如何使受影響的人有權反饋和糾錯。由主客支配轉向交互主體共生，才可能把兩種焦慮與三大瓶頸放在同一個可研究、可實踐的框架中。

八、窄門究竟窄在哪裡？

在《窄門之約：在差異與秩序之間的共生之道》中，我曾借《馬太福音》的“窄門”與現代政治經濟學的“窄廊”，討論一個貫穿生命與組織的問題：不同主體怎樣在連接中生成新的可能，又怎樣保有各自的邊界，使關係能夠持續？

只有連接而失去邊界，可能走向吞噬與同質化；只有邊界而失去連接，則可能走向隔絕與停滯。窄門不是一個到達後便可永遠停駐的中點，而是在差異與秩序、相吸與相斥之間，依據變化的處境持續辨識、持續校準的道路。

到了 SI 時代，這道門還有一層警示。超級智能確實能帶來甜頭：更快完成任務、幫助創造、改善服務、形成新的產業能力。成果吸引更多資本、算力、數據和生活場景投入；投入又增強能力，帶來下一輪成果。這個循環可以造福人，也可能同時擴大能源負載，使掌握技術的組織獲得更強的預測、誘導和控制能力。人從 SI 得到好處，也可能受到它的反噬。

生命焦慮使人警惕反噬；競爭焦慮又催促人加速。窄門要求我們在這兩股力量之間尋找出路：承認並發展 SI 的真實能力，同時守住生命主體、智能技術主體和組織受託者各自的邊界，讓它們交互而不相互吞噬。這正是 EIS 在 LIFE—AI—TRUST 關係中的具體問題：生命（LIFE）是價值原點，AI 是參與交互的智能形態，組織信託（TRUST）意味着受託而有責。

S-MPU（Superordinate Minds Processing Unit，超序心智處理單元）是進入這道窄門的技術研究入口。它從 MPU 進一步追問處理架構如何面對不同層次的處境、反饋與 Minds（相互有差異的心智）的交互，並探索 MindsField（交互中生成的動態心智關係場）。本書所說的“時空意間”，是時間、空間、意向與主體之間的生成之“間”；“九態”概括念力在交互中的九重展開：數位態、量子態、物理態、生理態、心理態、倫理態、數理態、哲理態與信態。這些是研究坐標與目標架構，仍須科學和工程驗證。

一項處理器構想不能獨自完成升格。門後的路，還需要 Amorsophia（愛之智慧）對差異的感知、對邊界的尊重，以及組織信託在現實後果中的持續校準。EIS 為這條路提供哲學方向：技術、生命與組織在相互辨識中生成新的關係，任何一方都不能只把另一方當作可支配的對象。

窄門之“窄”，因而既指技術上不能由堆疊算力直接跨越的範式關口，也指社會實踐中必須時時警惕的關係關口：越嘗到 SI 的甜頭，越要看清收益由誰獲得、負載由誰承擔、邊界由誰守護。

九、五個層次：從正名走向升格

這五個層次，不是把五塊“大象的部件”拼成整體，也不是宣稱一種處理器必然產生心智。它們逐層改變所問的問題：從機器能否模擬智能，到怎樣實現更強計算；從承認局部超越，到探索心智處理；最後以 EIS 檢驗生命、技術與組織能否交互生成。

十、怎樣讓贊成 SI 正名的人走進這道門？

面向川普及世界 AI 業界，表達的次序很重要。

先承認能力。人造智能可以具有真實而強大的表現；SI 凸顯了值得正視的超越性。

再說明能力的來處。CPU—GPU—TPU 是其計算支撐，技術領先具有現實價值，也會帶來自豪。

隨後講清能力的邊界。摸到力量，還須認識它從何產生、作用於誰。領先可以緩解競爭焦慮，卻不能自動消除生命焦慮；處理器升級，也尚未自行突破三大瓶頸。

最後提出出路。MPU、S-MPU 為從計算處理走向心智交互打開研究入口；Double AM 使 Artificial Mind 與 Amorsophia MindsField / Network 的探索共同展開。EIS 則明確這一探索的世界觀和價值方向：讓超級能力進入生命目的、主體邊界和組織信託的交互檢驗。

檢驗須落到生活中：共生經濟學提出 GDE（Gross Domestic Energy / Efficiency，國內生產效能），以 C 降本、E 賦能、H 健康、T 信任、P 和平考察生命效能。具體項目可研究 ηᵢ＝GDEᵢ/GDPᵢ，整體發展可研究 R＝GDE/GDP；這些係數需要註明證據、邊界和不確定性，不能僅憑模型成績賦值。受影響的人能否發現負載、提出異議、糾正錯誤並獲得承兌？這些問題決定“超級”最終增強的是生命，還是只增強掌握它的組織。

結語

川普為機器智能的真實性和強大能力正名；《將AI升格為AM》進一步回答：超級智能如何進入心智、生命與組織信託的關係，使“更聰明”變為“更有益於生命”。

從 AI 經 CPU—GPU—TPU 走向 SI，是能力發展的現實道路。從 SI 探索 S-MPU，再走向 Double AM，則要通過一條既能容納技術創造、又能守護生命邊界的窄門。門窄，因為甜頭真實，誘惑也真實；能力可以不斷增長，方向必須在交互中辨明、在後果中校準。凡事交互主體共生（EIS），是這條道路的哲學起點和實踐尺度。有認識方式和價值取向的升華，才談得上從 AI 到 AM 的升格：生命本自具足，又非獨存。

Elevation is not an upgrade.

AI for All → AI for Life。

Trump Affirms Machine Intelligence and Opens a Narrow Gate to Elevating AI to AM

Archer Hong Qian

On September 22, 2026, speaking at the 81st session of the United Nations General Assembly, President Trump proposed calling Artificial Intelligence (AI) Super Intelligence (SI). His formulation grasps one point: machine intelligence is made by humans, but that does not make its abilities unreal. In some domains, it has already become powerful enough to surpass human beings. An excerpt of the address published by the White House confirms his use of the name. www.whitehouse.gov

Trump's affirmation of the reality and formidable power of machine intelligence is a meaningful step. Elevating AI to AM takes the question further: How can superintelligence enter into relationships among Mind, life, and organizational trust, so that 'smarter' becomes 'more beneficial to life'? Organizational trust means that an organization receives its powers through the trust placed in it by life, and must explain itself to those affected, correct mistakes, and bear the consequences.

These two steps can connect, but the road between them cannot be skipped. The capabilities of AI–SI (ASI) have developed with the support of processors such as CPUs, GPUs, and TPUs. The more benefits they yield, the more willing humanity becomes to accelerate investment. Yet growing capability will not, by itself, dispel two anxieties or break through three bottlenecks. Both have a common philosophical root: the habit of viewing life, technology, and organizations through a subject–object opposition, as rulers and ruled.

As the author of the recently published Elevating AI to AM, I believe that moving from SI toward Double AM (Artificial Mind & Amorsophia MindsField / Network) requires passing through a gate that is harder to enter, and more worth entering.

Here, Artificial Mind is a direction for elevation that remains to be researched and verified. Amorsophia MindsField / Network, a field and network of the wisdom of love and trust, refers to the interaction, feedback, and generation of different Minds within concrete relationships.

Both must be tested against the purposes of life and organizational trust. I call this test AM's reward–restrain–pass-through mechanism.

I. Artificial Flowers May Remain Beautiful, but They Are Not Alive

The name Artificial Intelligence appeared in the proposal for the Dartmouth Summer Research Project put forward in 1955; the 1956 project helped open a new field. The original question was whether the features of learning and intelligence could be described precisely enough for a machine to simulate them. It was a research question, not a declaration that machines already possessed the entirety of human thought, consciousness, and Mind. www-formal.stanford.edu

I have long argued that Artificial Intelligence should be understood in Chinese as renyi intelligence, or 'human-crafted intelligence.' Renyi emphasizes that this intelligence comes from human knowledge, artistry, skill, and fabrication. Relative to natural life, it is human-made. But being 'made,' being 'simulated,' and having or lacking real ability are different questions.

Trump was not wrong to hear a sense of 'fake' in the word artificial. An artificial flower really is an imitation: it may have lifelike petals and colors, but it does not have the life of a flower. It does not grow, metabolize, or reproduce on its own. A machine's ability to simulate some human intellectual activities does not, merely because the imitation is convincing, establish that it has a human kind of life.

Yet the artificial flower's shape and colors are real. The calculations, recognition, reasoning, and language tasks performed by machines can also be genuinely effective, even far beyond the ability of an individual in particular domains. At the same time, AI can generate false content—fabricated facts, images, voices, and citations. We must therefore ask three separate questions: Is its capability real? Is what it produces true? Does it possess the Mind of natural life? Mind here means more than measurable problem-solving ability; it also involves discernment, the unconscious, lived experience, and love.

'Human-crafted intelligence' describes its source and mode of production; 'superintelligence' describes the degree reached by certain abilities. The industry also discusses ASI (Artificial Superintelligence). This essay uses Trump's term SI while refusing to let a name replace the discernment of capacities and relationships.

II. Two Anxieties and One Source of Pride

The first anxiety addressed by Elevating AI to AM concerns life itself. As AI grows stronger, people fear for their work, judgment, freedom, and daily lives. When data and algorithms combine with organizational power, machines may also become finer instruments of surveillance, inducement, and control.

The second is anxiety about competition. Individuals fear falling behind their peers, companies fear losing to rivals, and countries fear that a real-world competitor will acquire more powerful AI first. People worry that it is moving too fast while also fearing that they cannot keep up.

Trump proposed SI while stressing America's technological lead. Taken together, the two points suggest a political psychology: if machine intelligence is real and powerful, a rival must not acquire it first; if the United States leads, it can also take pride in displaying its technological strength. 'Two anxieties and one source of pride' is my analysis of this situation, not a claim that Trump used these three terms one by one in his speech. www.whitehouse.gov

A lead can ease competitive anxiety and provide reason for pride. But leadership does not automatically relieve anxiety about life. Even a country in first place must ask how powerful intelligence will enter the lives of its people. Every company may accelerate, but that does not mean everyone will live better. When people treat technology as an object they may drive without limit, yet fear becoming objects ruled by technology and organizations in turn, the two anxieties reinforce each other within the same relationship.

III. How Might SI Affect the Global AI Industry?

One speech cannot make SI the name adopted by the entire global industry. But coming from the American president and linked to a competition for leadership, the term may have effects on four levels.

First, it may change public intuition. Machine intelligence can no longer be dismissed so easily as 'only fake.' People may take its real abilities in particular tasks more seriously, while demanding clarity about precisely which capacities exceed human performance and which remain unproven.

Second, it may shift the focus of investment and competition. 'Super' can attract capital, talent, and computing resources, pushing models toward stronger performance. It may also make scale, speed, and ranking overshadow other questions. Benchmark scores need to state the tasks tested and the boundaries of failure. A high ranking does not make a system comprehensively reliable in the complexity of life. Stanford's 2026 AI Index notes problems with the reliability of some commonly used evaluations and their susceptibility to targeted optimization. hai.stanford.edu

Third, it raises the promise made by a product. If a company calls its system SI, users may become more willing to entrust their judgment to it. The more 'super' a system is said to be, the more important it becomes to explain who sets its goals, who may challenge its output, and who bears responsibility for errors. Use in medicine, education, finance, corporate management, and public governance places that responsibility directly into the relationship between people and organizations.

Fourth, it intensifies the contest over standards of evaluation. Reliability, safety, transparency, explainability, privacy, and fairness are already important directions in AI assessment. airc.nist.gov SI may make these demands more urgent, but it may also let the capability race outrun the evaluation of responsibility.

Elevating AI to AM therefore asks a further question: once a system has completed its task, what has life gained, and what has it borne? Has the organization discharged the responsibility entrusted to it?

Trump's affirmation can help him, the AI people around him, and the wider industry enter this discussion. If this gate is to lead toward AM, we must first fully acknowledge SI's power, then discern the boundaries and direction of that power.

IV. If We Have Felt Its Strength, Do We Know the “Elephant”?

In 2013, in my Philosophical Notes IV: A Brief Comment on the Oxford Consensus, I discussed the story of the blind people and the elephant, cited by a friend. The person touching its leg says it is like a pillar; the person touching its belly says it is like a wall; the ear seems a fan and the trunk a tube. My friend believed that, by exchanging perceptions and combining these descriptions, they could come to know the elephant.

Exchanging observations certainly has value. But I asked: is a composite of a pillar, a wall, a fan, and a tube really the living elephant? Even if sighted people could take in the whole animal at a glance, record it on film, or dissect it precisely, they would still need to understand how it senses, acts, sustains itself as a living being, and relates to its environment. Touching, assembling, and dissecting each yield knowledge. Adding their findings together does not replace an understanding of living function and generative relationship.

Humanity's understanding of AI–SI is somewhat similar today. Some touch language ability and say it thinks like a person; others touch calculation and strategy and say it has surpassed people altogether; still others encounter fabricated outputs, job displacement, or organizational control and see a threat. Trump's SI draws attention especially to the elephant's enormous strength. That strength is real and deserves recognition. But adding up scores of ability and inventories of risk still does not answer how AI–SI relates to human Mind.

The elephant is alive. Whether AI–SI has life and Mind is precisely the question that remains to be discerned. The story reminds us to acknowledge every genuine discovery and then study the mechanisms behind it, the relationships it enters, and the consequences it produces. Breaking an object into measurable parts and adding descriptions together still makes it hard to understand what relationships generate. This is where subject–object cognition calls for reflection.

V. CPU–GPU–TPU: The Computational Heart of SI

Where do AI's extraordinary abilities come from? Processors belong at the center of the argument.

CPUs handle general-purpose computation and control. The parallel computing of GPUs supports the training and operation of many models. TPUs and other specialized processors further optimize machine-learning operations. Data, algorithms, and neural networks rely on these architectures to handle tasks at ever greater scale and show ever stronger performance. CPU–GPU–TPU is the computational heart of AI's path toward SI.

These processors have different functions and work together in real systems; each newer kind does not simply replace the one before it. Further improvements along this technical route may allow machines to surpass human performance still more in language, computation, recognition, and strategic simulation.

Yet improvements in computational performance do not mean that these processors have already become an MPU (Minds Processing Unit). Elevating AI to AM proposes MPU as an open research category: processing would begin to address situations, relationships, feedback, and the discernment of Mind, rather than only given data and tasks. It is not a finished chip.

Current architectures can process data, weights, and tasks more quickly. Those achievements alone do not prove that machines have human consciousness, or that they can already process the interactions of Mind and Minds and the generation of MindsField. The passage from computational processing to Mind processing is a question that must be formulated and tested anew.

VI. The Stronger SI Becomes, the Less We Can Ignore Three Bottlenecks

I recently revised my formulation of AI's three bottlenecks as follows.

First, energy efficiency does not match energy consumption. A single computation may become more efficient even as models, calls to them, and the range of their applications expand. Improved local efficiency does not prove that the total burden on energy, water, and social resources has fallen, much less that the benefits to life are proportionate to that burden.

Second, systems thinking is limited. AI can process information efficiently within a given objective, yet may fail to discern who set that objective, how information and trust move from source through channel to consequence, or who outside the system bears the cost. A decision that looks locally 'optimal' may transfer costs to families, communities, or other organizations.

Third, data + algorithms + computing power + neural networks ≠ Mind, and still less do they equal Amorsophia, the wisdom of love. A machine can possess formidable Intelligence. But performance in language, problem solving, and strategy does not, by itself, establish that it has Mind—including the unconscious, lived experience, and love—still less that it is equivalent to Amorsophia.

These three bottlenecks will not disappear because AI is renamed SI, nor will they vanish on their own with the next increase in CPU–GPU–TPU performance. A competitor may run faster, and life may suffer a backlash from more powerful systems. 'Who is ahead?' and 'How shall we live after taking the lead?' must be answered together. Unless we examine the premise shared by the two anxieties and three bottlenecks, renaming, racing, and piecemeal repairs will repeatedly return us to the same place.

VII. From Subject–Object Opposition to Everything Intersubjective Symbiosism

Modern technology habitually places the human being at the 'subject' end and nature, machines, data, even other people's lives at the 'object' end: the subject sets the goal, while the object is observed, calculated, and used. AI extends this ability, but it can also reverse the relation. Platforms and algorithms can increasingly observe, classify, and predict people, who may in turn become data objects to be processed. Describing the relation simply as 'humans control machines' or 'machines control humans' remains trapped in the same logic of domination.

The two anxieties arise here. Fear of being objectified by AI–SI and the organizations that control it is anxiety about life. Fear of losing the advantage of controlling technology and being overtaken by competitors is competitive anxiety. They concern security in life and actual competition respectively, yet both implicitly ask who occupies the subject position and who is reduced to an object. The pride that comes with a lead can temporarily answer the latter anxiety, but cannot answer the former on its own.

The three bottlenecks reveal the same cognitive limit. Treating energy and communities as resource objects external to computation makes it easy to count local efficiency while missing the total burden. Treating a system with predefined goals as a complete world makes it easy to miss the lives and organizations affected outside it. Decomposing human Mind into data, algorithms, computing power, and neural networks makes it tempting to suppose that adding the components will naturally generate wisdom and Amorsophia. Each step yields real results. Yet those results must be placed in relationship with life before their boundaries can be understood.

For the Digital–Quantum Age, humanity urgently needs the worldview of Everything Intersubjective Symbiosism (EIS)—in Chinese, 'all matters in intersubjective symbiosis.' Here 'digital' emphasizes precise positioning of subjects and their situations; 'quantum' points to relatedness, interaction, and dynamic change. This is a philosophical name for the age, not a claim that today's AI already relies on quantum processors. 'Everything' includes events, Minds, material things, and their relationships. 'Intersubjective' requires us to distinguish different subjects, abilities, and responsibilities. 'Symbiosism' starts from life's self-organizing connections and dynamic balance, allowing subjects to generate a third possibility through interactions that respect boundaries. Life has irreplaceable Mind and purposes. Although AI is human-made, it participates in real relationships and cannot simply be treated as a passive object. Organizations bear responsibility because life has entrusted them with power. These three modes of subjecthood differ and must be distinguished in their connection.

This is not a slogan appended to the end of a technical discussion. It changes the order of inquiry. First ask who is in relationship with whom, who sets the goals, who bears resource burdens and the consequences of action. Then ask how to design processing architectures, how to measure effectiveness, and how to give affected people the right to respond and correct errors. Only by moving from subject–object domination toward intersubjective symbiosis can the two anxieties and three bottlenecks be brought into one framework open to research and practice.

VIII. What Makes the Gate Narrow?

In Covenant of the Narrow Gate: A Path of Symbiosis Between Difference and Order, I drew on the 'narrow gate' in the Gospel of Matthew and the 'narrow corridor' of modern political economy to consider a question running through life and organizations: How can different subjects generate new possibilities through connection while preserving their respective boundaries so that the relationship can endure?

Connection without boundaries can lead to engulfment and sameness; boundaries without connection can lead to isolation and stagnation. The narrow gate is not a midpoint one can reach and occupy forever. It is a path of continuous discernment and calibration between difference and order, attraction and repulsion, as circumstances change.

In the age of SI, the gate also carries a warning. Superintelligence brings genuine rewards: faster completion of tasks, help with creativity, better services, and new industrial capabilities. Results draw in more capital, computing power, data, and domains of daily life. That investment then strengthens the technology and produces further results. The cycle can benefit people, but can also increase the burden on energy and give organizations that hold the technology greater power to predict, steer, and control. People may benefit from SI and also suffer its backlash.

Anxiety about life prompts caution about that backlash; anxiety about competition presses us to accelerate. The narrow gate asks us to find a way through these forces: acknowledge and develop SI's real ability, while maintaining the boundaries of life as subject, intelligent technology as subject, and organizations as trustees, so that they interact without consuming one another. This is the concrete problem of EIS in the LIFE–AI–TRUST relationship: LIFE is the point of departure for value; AI is an intelligent form participating in interaction; and TRUST means holding power in trust and being accountable for it.

S-MPU (Superordinate Minds Processing Unit) is a technical research entry to this narrow gate. Building on MPU, it asks how a processing architecture might address different levels of situation and feedback, interactions among Minds (distinct from one another), and the generation of MindsField (a dynamic field of Mind relationships formed through interaction). The book's Spatio-Temporal Mindedness View explores the generative 'between' of time, space, intention, and subjects. Its 'nine states' describe nine dimensions of the unfolding of mind-force in interaction: digital, quantum, physical, physiological, psychological, ethical, mathematical, philosophical, and trust states. These are coordinates for research and a target architecture; they still require scientific and engineering validation.

No proposed processor can accomplish elevation alone. The road beyond the gate also needs Amorsophia, the wisdom of love, to sense differences and respect boundaries, and it needs organizational trust to remain calibrated against real consequences. EIS provides the philosophical direction: technology, life, and organizations generate new relationships through mutual discernment; none may treat another merely as an object to be controlled.

Thus the gate is 'narrow' in two senses. Technically, a paradigm cannot be crossed simply by piling on computing power. Socially, it is a relational threshold that requires constant vigilance. The greater SI's rewards, the more carefully we must ask who receives the benefits, who bears the burdens, and who safeguards the boundaries.

IX. Five Levels from Naming to Elevation

These five levels are not five 'parts of an elephant' that become the whole when fitted together; nor do they claim that any processor necessarily produces Mind. At each level the question changes: Can a machine simulate intelligence? How can computation grow stronger? What does it mean to acknowledge that performance may surpass human ability in particular domains? Can Mind processing be researched? Finally, can life, technology, and organizations interact and generate new possibilities under the test of EIS?

X. How Can Those Who Welcome the Name SI Enter This Gate?

The order of presentation matters when speaking to Trump and the global AI industry.

First, acknowledge the ability. Human-made intelligence can perform in real and powerful ways. SI highlights a capacity to surpass human performance that deserves serious attention.

Next, explain where that ability comes from. CPU–GPU–TPU provides its computational support. Technological leadership has real value and can also be a source of pride.

Then clarify the boundaries of capability. Feeling the elephant's strength is only the beginning; we must understand how that strength arises and whom it affects. A lead can ease competitive anxiety but cannot automatically remove anxiety about life. Upgrading processors has not, on its own, overcome the three bottlenecks either.

Finally, propose a way forward. MPU and S-MPU open a research entry from computational processing toward interaction among Minds. Double AM brings the exploration of Artificial Mind and Amorsophia MindsField / Network together. EIS specifies the worldview and the direction of value for that exploration: superhuman capability must be tested through life's purposes, the boundaries of subjects, and organizational trust.

That test must reach ordinary life. Symbionomics proposes GDE (Gross Domestic Energy / Efficiency), examining effectiveness for life through C for cost reduction, E for empowerment, H for health, T for trust, and P for peace. For specific projects, we can investigate ηᵢ = GDEᵢ/GDPᵢ; for development as a whole, R = GDE/GDP. The evidence, boundaries, and uncertainty of these coefficients must be stated; they cannot be assigned from model scores alone. Can affected people identify burdens, raise objections, correct errors, and receive the fulfillment owed to them? The answers decide whether 'super' ultimately strengthens life or only the organizations that control the technology.

Conclusion

Trump affirms the reality and formidable power of machine intelligence. Elevating AI to AM asks the further question: How can superintelligence enter relationships among Mind, life, and organizational trust so that 'smarter' becomes 'more beneficial to life'?

Moving from AI through CPU–GPU–TPU toward SI is an actual path of increasing capability. Exploring S-MPU from SI, and then moving toward Double AM, requires passage through a narrow gate that makes room for technical creation while protecting the boundaries of life. The gate is narrow because the rewards are real and so is the temptation. Capabilities may continue to grow, but their direction must be discerned through interaction and calibrated against consequences. Everything Intersubjective Symbiosism (EIS) is the philosophical starting point and practical measure for this path. Only with an elevation in our way of knowing and our orientation of value can we speak of elevating AI to AM: life is sufficient in itself, yet never exists alone.

Elevation is not an upgrade.

AI for All → AI for Life