評論
打印
Finance & economics
財經版塊
ChatGPT at two
兩年后的ChatGPT
Dotcom dreaming
網絡夢想
Will the AI age offer the same benefits as the computer age?
人工智能時代會帶來像計算機時代一樣的紅利嗎？
Almost two years have passed since OpenAI released GPT-3.5 to great fanfare.
自OpenAI發布GPT-3.5并引起轟動，已經過去差不多兩年了。
Bill Gates, co-founder of Microsoft, compared the technology’s arrival to his first encounter with the graphical user interface—a breakthrough that reshaped personal computing—in the 1980s.
微軟的聯合創始人比爾·蓋茨將這項技術的到來與他在20世紀80年代首次看到圖形用戶界面進行類比——后者是重塑了個人計算機技術的突破。
Others predicted that generative artificial intelligence (AI) would rapidly transform economies around the world, leaving many millions unemployed.
其他人預測，生成式人工智能將迅速改變世界各地的經濟，導致數百萬人失業。
Yet despite the hype and the worries, AI’s impact has been muted thus far.
然而，盡管有炒作和各種擔憂，但人工智能的影響迄今為止一直很微弱。
According to America’s Census Bureau, only 6% of businesses use AI to produce goods and services.
根據美國人口普查局的數據，只有6%的企業使用人工智能來生產商品和提供服務。
Output and labour-productivity growth, meanwhile, remain far below the soaring heights of the computer age in the 1990s.
與此同時，產出和勞動生產率的增長仍遠低于20世紀90年代計算機時代的飆升高度。
Why has AI so far failed to live up to its promise?
為什么人工智能到目前為止沒能實現承諾？
Lessons from the computer age can shed light on the question.
計算機時代的經驗教訓可以為這個問題提供啟發。
As with AI today, the early years of the computer age were marked by predictions of economic transformation.
就像今天的人工智能一樣，計算機時代早期的特點也是人們預測經濟將轉型。
In 1965 Herbert Simon, a giant of computer science, declared that “machines will be capable within 20 years of doing any work that a man can do.”
1965年，計算機科學巨擘赫伯特·西蒙宣稱：“機器將在20年內能夠完成人類能做的任何工作。”
Two decades after Simon’s prediction, the promised productivity revolution remained elusive.
在西蒙做出預測二十年后，計算機所承諾的生產力革命仍然難尋其蹤。
In 1987 Robert Solow, a Nobel laureate, famously quipped that “you can see the computer age everywhere but in the productivity statistics.”
1987年，諾貝爾獎得主羅伯特·索洛說了有名的金句：“你可以在生產力統計數據之外的任何地方看到計算機時代。”
Only in the late 1990s did the economic transformation at last materialise, leading Solow to acknowledge—three decades after the initial exuberance—that computers had begun to reshape the economy.
直到20世紀90年代末，經濟轉型才終于實現，讓索洛在最初的興奮過去三十年后才承認，計算機已經開始重塑經濟了。
Three main factors contributed to the eventual arrival of a computer-age productivity boom: companies ramped up investment in information technology, computer and software prices fell rapidly, and bosses found new ways to integrate the tech into their operations.
三個主要因素促使計算機時代生產力大繁榮最終到來：企業加大了對信息技術的投資，計算機和軟件價格迅速下降，老板們找到了將計算機技術融入其業務的新方法。
Are these factors in evidence today?
這些因素在今天是否明顯？
Begin with IT investment.
首先從信息技術投資開始。
Starting in 1995, firms ramped up spending on computer hardware, network infrastructure and software.
從1995年起，企業加大了對計算機硬件、網絡基礎設施、軟件的支出。
Between 1995 and 2000, their investment in information-processing equipment and software rose by an average of 20% a year in real terms.
在1995年至2000年期間，企業在信息處理設備和軟件方面的投資按實際價值計算，平均每年增長20%。
Research by Kevin Stiroh of the Federal Reserve Bank of New York has found that firms were investing nearly $400bn in such technologies by 1999, accounting for over 30% of all non-residential fixed investment.
紐約聯邦儲備銀行的凱文·斯蒂羅的調查發現，到1999年，企業在這些技術上的投資接近4000億美元，占所有非住宅固定投資的30%以上。
By contrast, recent capital expenditure has been underwhelming.
與之相反，最近的資本支出一直乏力。
Over the past two years, business investment in information-processing equipment and software has grown by around 4% a year.
在過去兩年中，企業在信息處理設備和軟件方面的投資每年增長約4%。
AI investment may be more focused on intangible assets, such as algorithms and data, which are more difficult to measure than physical capital.
人工智能投資可能更側重于無形資產，如算法和數據，這些資產比實物資本更難以衡量。
Payments to startups for custom tools may show up as operating expenses in the statistics, for example.
例如，支付給初創公司的定制工具費用可能會在統計數據中顯示為運營費用。
Even so, you would expect at least a rise in software investment.
即便如此，你至少也會期望軟件投資有所增長。
Instead, spending on both pre-packaged commercial software—such as Microsoft 365—and custom-built systems, including AI tools tailored to specific workflows, is surprisingly low.
相反，在預包裝商業軟件（如Microsoft365）和定制系統（包括針對特定工作流程定制的人工智能工具）上的支出卻低得驚人。
Growth in software investment over the past year was about three times lower than in the late 1990s in real terms, and remains well below the long-term average.
過去一年，軟件投資的增長實際上是20世紀90年代末的三分之一，并且遠低于長期平均水平。
