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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世纪90年代后半期，计算机硬件和软件的质量调整价格也大幅下降。
From 1995 to 2000 prices for information-processing equipment and software dropped by a third, producing cheaper and better computers.
从1995年到2000年，信息处理设备和软件的价格下降了三分之一，生产出了更便宜、更好的计算机。
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.
人工智能时代尚未看到相应的价格下降：在过去五年，软件和信息处理设备的价格几乎没有变动。
Indeed, in the most recent quarter, the price index for these goods rose at an annualised rate of 4%.
事实上在最近一个季度，这些商品的价格指数以年化4%的速度上涨。
Even as the underlying technology is becoming cheaper, middlemen who repackage AI tools are increasingly adding margins and driving up prices.
即使底层技术变得越来越便宜，重新包装人工智能工具的中间商也在不断增加利润并抬高价格。
What about the final ingredient in the economic revolution of the 1990s?
那么20世纪90年代经济革命的最后一个要素情况如何呢？
For a technology to provide productivity gains, companies must retool operations and business models to integrate it.
要使一项技术能够提高生产力，企业必须重组运营和商业模式以整合这种技术。
Consider the example of Walmart.
考虑一下沃尔玛的例子。
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世纪90年代，这家零售商通过在其业务中嵌入一个新的软件系统——零售链接——而提高了生产力，使供应商能够实时访问销售和库存数据。
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.
在当今，对人工智能的采用仍然主要局限于在现有业务中的狭窄应用，例如金融服务公司使用人工智能应用程序进行诈骗检测。
Most firms do not have the data infrastructure required to train custom firm-specific models.
大多数公司没有训练定制的、公司特定的模型所需的数据基础设施。
To unlock AI’s full potential, more fundamental changes will be required.
为了充分释放人工智能的潜力，进行更根本的变革是必需的。
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.
鉴于这些限制因素，回想一下麻省理工学院经济学家鲁迪·多恩布什的话或许是明智的：在经济学中，事情发生的速度比你想象的事情“将会”发生的速度要慢，然后又比你想象的事情“能够”发生的速度要快。
AI may eventually produce extraordinary productivity growth, but at present it appears to be some distance from the take-off experienced in the 1990s.
人工智能最终可能会带来非凡的生产力增长，但目前看来，距离20世纪90年代的腾飞还有一段距离。
Perhaps a more fitting comparison is to the 1970s—a period when technological promise mingled with disappointing productivity growth.
或许更恰当的比较是与20世纪70年代进行对比，那个时期，技术承诺的前景与令人失望的生产率增长交织在一起。
The memory chip and silicon microprocessor, which powered the personal computer, were introduced around 1970.
内存芯片和硅微处理器（为个人计算机提供了动力）大约在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，世界上使用计算机的企业还不到10%。
As the world moved into the information age with the arrival of email, mobile phones and the internet, productivity growth remained stubbornly low.
随着电子邮件、手机和互联网的到来，世界进入了信息时代，但生产率增长仍然顽固地保持在低水平。
From 1975 to 1994 labour productivity in America averaged a lacklustre 1.7%.
从1975年到1994年，美国的劳动生产率平均只有平平无奇的1.7%。
Then things finally got going.
之后情况才终于有了进展。
The AI revolution seems to be following a similar path.
人工智能革命似乎正在遵循类似的路径。
