Two Thoughts on AI
一、美國德克薩斯大學教授James K. Galbraith從他認為1929年華爾街股災與隨之大蕭條的真正成因論證2026年AI科技浪潮正踩在歷史的韻腳上。
首先他列舉，過去一個世紀以來經濟學界對這兩起事件的解釋莫衷一是的紛雜學說。如Hyman Minsky的「金融不穩定假說」，認為資本主義天生脆弱；或William K. Black的「控制性欺詐」，認為監管鬆綁讓內部人牟利。或John Maynard Keynes將危機歸咎於商人對利潤預期的「動物精神」崩潰；或Milton Friedman與Anna Schwartz歸咎於聯準會未能阻止貨幣供應縮減；Ben Bernanke與Christina Romer強調市場不確定性飆升；或如Larry Summers模糊歸因於「交換機制失敗」。
Galbraith則認為，1929年股災的真正導火線，是一場顛覆性的能源技術革命。二十世紀初，美國經濟實體與金融資產高度依賴煤炭、蒸汽機、鋼鐵與龐大的跨州鐵路網絡。然而，1929年十月下旬，美國德州、奧克拉荷馬州與加州相繼宣布發現天量石油儲備。媒體陸續報導標準石油公司因嚴重過產而將單價腰斬，並宣布大幅縮減庫存。
為什麼天量廉宜優質能源的發現不但沒有帶來應有的經濟繁榮，令人意外地反而引發數十億美元級的金融損失，演變成歷史上的「黑色星期四」與「黑色星期二」？
原因在于，金融市場定價反映的是「既有產業體系」的未來獲利預期。當時銀行持有的龐大資產是煤炭與鐵路債券，而企業利潤仰賴鐵路運輸網絡與燃煤發電系統。石油儲備的發現意味著這些重資產投資將在未來面臨無預警的淘汰。預期利潤瞬間歸零，企業停止資本投資，農工產品價格崩跌，銀行無法回收農業與鐵路貸款而遭遇擠兌倒閉，實體經濟連鎖塌陷，大蕭條由此展開。
Galbraith進一步點出，他認為現在AI技術如果真的是一場工業革命等級進步，則很可能重複1929年蕭條。
這邊我用我的理解再說得更簡單一點：
從經濟學角度，競爭之下，AI涉及的許多行業都會因此被迫降低獲利預期，從而降低資產價格。而這些資產價格（無論是有形或無形財產，包含土地廠房機械，乃至於智慧財產權、公司債與股票）如果都藉由各種金融抵押借貸交織成複雜的金融市場，則更進一步以這些東西為基礎的更複雜的金融商品（如信用交換credit swap）價格都被迫要向下修正，這些資產價格修正本身又會進一步加強宏觀預期從而改變經濟行為，形成一個類似1929或2008年的負面循環蕭條。
但另一方面，新的AI技術帶來的實質收入增長是否能趕上被吹出來的預期收入泡沫？如果揭露的數據讓越來越多人相信增長速度趕不上，加上客觀利率環境轉劣，則AI泡沫就要破裂。
而從今年中以來，我們看到AI相關公司，尤其是neo-cloud產業的借貸利率和借貸難度均大幅上升，其實把此看為風險貼水，已經提示市場越來越多人轉為不相信了。
二、中美AI競爭陰謀論
這個陰謀論我認為可信度不高，但很有趣。
依據這幾個月越來越清楚的數據，我們可以看出AI科技革命實質收入增長其實遠不如這些鼓吹者所畫的空中樓閣。
陰謀論認為這就是為何中國政府並未真的「用盡一切資源」在「中美AI競爭」這件事上，更多的是針對中國自身產業環境需求去運用與發展AI技術。
而中美AI競爭上，中國方面一直也將始終保持「快要追上美國」的步調，來讓美國政客有誘因和恐懼、產業界有藉口，調撥大量美國國家資源在這個其實回報率很低的大語言模型AI發展上。
簡言之，中國正在引誘美國掉入當年蘇聯踩中的「星球大戰」陷阱。
以上兩個關於AI的思考，供各位參考。
I. A Historical Echo: The 1929 Crash and the 2026 AI Wave
Professor James K. Galbraith of the University of Texas argues that—based on his theory regarding the true root causes of the 1929 Wall Street Crash and the ensuing Great Depression—the 2026 AI tech wave is stepping on the same historical rhymes.
First, he lists the complex and conflicting theories the economic community has proposed over the past century to explain these events. Examples include Hyman Minsky’s “Financial Instability Hypothesis" (capitalism is inherently fragile); William K. Black’s “Controlled Fraud" (deregulation allowing insider profit); John Maynard Keynes, who blamed the collapse of businessmen’s profit expectations driven by “animal spirits"; Milton Friedman and Anna Schwartz, who blamed the Fed for failing to stop the money supply contraction; Ben Bernanke and Christina Romer, emphasizing the spike in market uncertainty; or Larry Summers, who vaguely attributed the crisis to a “failure of exchange mechanisms."
Galbraith, however, argues that the real trigger for the 1929 crash was a disruptive energy technology revolution. In the early 20th century, the physical economy and financial assets of the US were heavily dependent on coal, steam engines, steel, and a massive cross-state railroad network. However, in late October 1929, Texas, Oklahoma, and California successively announced the discovery of massive oil reserves. Media reports detailed how Standard Oil was slashing prices due to overproduction and cutting inventory.
Why did the discovery of massive, cheap, high-quality energy not bring the expected economic prosperity, but unexpectedly trigger billions in financial losses, evolving into historical “Black Thursday" and “Black Tuesday"? The answer lies in the fact that financial market pricing reflects the future profit expectations of the “existing industrial system." At the time, banks held massive assets in coal and railroad bonds, while corporate profits relied on the rail transport network and coal power systems. The oil discoveries meant that these heavy-asset investments faced unannounced obsolescence. Expected profits hit zero overnight. Companies stopped capital investment, prices for agricultural and industrial goods collapsed, banks couldn’t recover loans (encountering bank runs), and the real economy suffered a chain reaction collapse—the Great Depression began.
Galbraith points out that if today’s AI technology represents an industrial-revolution-level advancement, it could very well repeat the depression of 1929.
To simplify this with my own understanding:
From an economic perspective, competition forces many industries impacted by AI to lower their profit expectations, thereby reducing asset prices. If these asset prices (whether tangible property, land, factories, machinery, or intangible property such as intellectual property, corporate bonds, and stocks) are interwoven into a complex financial market via collateral and borrowing, it forces a downward revision of even more complex financial derivatives based on them (such as credit swaps). This asset price correction strengthens negative macro expectations and alters economic behavior, creating a negative feedback loop deflation similar to 1929 or 2008.
On the other hand, can the actual income growth generated by new AI technology catch up with the hyped revenue bubbles? If releasing data convinces more people that growth speed cannot keep pace, combined with a worsening objective interest rate environment, the AI bubble will burst. Since mid-year, borrowing rates and the difficulty of borrowing for AI-related companies—especially in the “neo-cloud" sector—have risen sharply. Viewing this as a risk premium indicates that more and more in the market are losing confidence.
II. The Conspiracy of China-US AI Competition
I believe this conspiracy theory has low credibility, but it is interesting.
According to clearer data over recent months, we can see that the actual income growth from the AI tech revolution is far below the “castles in the air" drawn by its promoters.
The conspiracy theory suggests this is why the Chinese government has not truly expended “all resources" on the “US-China AI competition." Instead, China is applying and developing AI tech mainly based on its own domestic industrial environment and needs.
In the AI competition, China maintains a pace of “just about catching up to the US" to tempt and frighten US politicians, and provide industry justifications, to deploy vast amounts of US national resources into developing LLMs—which actually offer low returns.
In short, China is tempting the US to fall into the “Star Wars" trap that the Soviet Union fell into decades ago.
The above are two considerations regarding AI for everyone’s reference.
