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The second half of the 1990s also witnessed a dramatic fall in the quality-adjusted price of computer hardware and software.
20世紀90年代后半期，計算機硬件和軟件的質量調整價格也大幅下降。
From 1995 to 2000 prices for information-processing equipment and software dropped by a third, producing cheaper and better computers.
從1995年到2000年，信息處理設備和軟件的價格下降了三分之一，生產出了更便宜、更好的計算機。
The AI era has yet to see a corresponding decrease in prices: over the past five years, those for software and information-processing equipment have barely budged.
人工智能時代尚未看到相應的價格下降：在過去五年，軟件和信息處理設備的價格幾乎沒有變動。
Indeed, in the most recent quarter, the price index for these goods rose at an annualised rate of 4%.
事實上在最近一個季度，這些商品的價格指數以年化4%的速度上漲。
Even as the underlying technology is becoming cheaper, middlemen who repackage AI tools are increasingly adding margins and driving up prices.
即使底層技術變得越來越便宜，重新包裝人工智能工具的中間商也在不斷增加利潤并抬高價格。
What about the final ingredient in the economic revolution of the 1990s?
那么20世紀90年代經濟革命的最后一個要素情況如何呢？
For a technology to provide productivity gains, companies must retool operations and business models to integrate it.
要使一項技術能夠提高生產力，企業必須重組運營和商業模式以整合這種技術。
Consider the example of Walmart.
考慮一下沃爾瑪的例子。
In the 1990s the retailer boosted productivity by embedding a new software system—Retail Link—into its operations, granting suppliers real-time access to sales and inventory data.
在20世紀90年代，這家零售商通過在其業務中嵌入一個新的軟件系統——零售鏈接——而提高了生產力，使供應商能夠實時訪問銷售和庫存數據。
AI adoption today remains largely confined to narrow applications within existing operations, such as a financial-services firm using an AI app for fraud detection.
在當今，對人工智能的采用仍然主要局限于在現有業務中的狹窄應用，例如金融服務公司使用人工智能應用程序進行詐騙檢測。
Most firms do not have the data infrastructure required to train custom firm-specific models.
大多數公司沒有訓練定制的、公司特定的模型所需的數據基礎設施。
To unlock AI’s full potential, more fundamental changes will be required.
為了充分釋放人工智能的潛力，進行更根本的變革是必需的。
Given these constraints, it might be prudent to recall the words of Rudi Dornbusch, an economist who spent his career at the Massachusetts Institute of Technology: that in economics things happen slower than you thought they would and then faster than you thought they could.
鑒于這些限制因素，回想一下麻省理工學院經濟學家魯迪·多恩布什的話或許是明智的：在經濟學中，事情發生的速度比你想象的事情“將會”發生的速度要慢，然后又比你想象的事情“能夠”發生的速度要快。
AI may eventually produce extraordinary productivity growth, but at present it appears to be some distance from the take-off experienced in the 1990s.
人工智能最終可能會帶來非凡的生產力增長，但目前看來，距離20世紀90年代的騰飛還有一段距離。
Perhaps a more fitting comparison is to the 1970s—a period when technological promise mingled with disappointing productivity growth.
或許更恰當的比較是與20世紀70年代進行對比，那個時期，技術承諾的前景與令人失望的生產率增長交織在一起。
The memory chip and silicon microprocessor, which powered the personal computer, were introduced around 1970.
內存芯片和硅微處理器（為個人計算機提供了動力）大約在1970年左右被推出。
Yet 20 years later, less than 10% of the world’s businesses were using computers.
然而20年后，世界上使用計算機的企業還不到10%。
As the world moved into the information age with the arrival of email, mobile phones and the internet, productivity growth remained stubbornly low.
隨著電子郵件、手機和互聯網的到來，世界進入了信息時代，但生產率增長仍然頑固地保持在低水平。
From 1975 to 1994 labour productivity in America averaged a lacklustre 1.7%.
從1975年到1994年，美國的勞動生產率平均只有平平無奇的1.7%。
Then things finally got going.
之后情況才終于有了進展。
The AI revolution seems to be following a similar path.
人工智能革命似乎正在遵循類似的路徑。
