《经济学人》双语：未来生成式AI行业的3股力量?
原文标题：
Beyond the hype
ChatGPT mania may be cooling, but a serious new industry is taking shape
Three forces will shape the business of generative AI
炒作过后
ChatGPT 热度正在降温，但一个严肃的新行业正在形成
三股力量将塑造生成式人工智能行业
[Paragraph 1]
THE FIRST wave of excitement about generative artificial intelligence (AI) was like nothing else the world had seen.
生成式人工智能（AI）的第一波热潮在世界范围内前所未见。
Within two months of its launch in November 2022, ChatGPT had racked up 100m users.
ChatGPT在2022年11月推出后两个月内就获得了1亿用户。
Internet searches for “artificial intelligence” surged; more than $40bn in venture capital flowed into AI firms in the first half of this year alone.
互联网上关于 "人工智能 "的搜索量激增；仅今年上半年就有400多亿美元的风险资本流入人工智能公司。
[Paragraph 2]
The craze for consumer experimentation has since cooled a little: ChatGPT use has fallen and fewer people are Googling “AI”.
此后，消费者的尝试热潮略有降温：ChatGPT 的使用量下降了，谷歌上“AI”的搜索量也下降了。
Son Masayoshi, a Japanese investor notorious for diving into already frothy markets, is thought to be interested in investing in OpenAI, ChatGPT’s creator.
孙正义是日本著名的投资家，因涉足已经泡沫化的市场而成为焦点，据称他有兴趣投资 ChatGPT 的母公司OpenAI。
But a second, more serious phase is beginning. An entirely new industry centred on supercharged AI models is taking shape.
但AI更重要的第二个阶段开始了。一个全新的以强化版AI模型为中心的产业正在形成。
Three forces will determine what it eventually looks like—and whether OpenAI stays dominant, or other players prevail.
三股力量将决定AI产业的最终样貌--以及OpenAI是否能保持主导地位，还是其他参与者胜出。
[Paragraph 3]
The first factor is computing power, the cost of which is forcing model-builders to become more efficient.
第一股力量是计算能力，算力成本迫使模型构建者提高效率。
Faced with the eye-watering costs of training and running more powerful models, for instance, OpenAI is not yet training its next big model, GPT-5, but GPT-4.5 instead, a more efficient version of its current leading product.
例如，面对训练和运行更强大模型的惊人成本，OpenAI目前没有训练它下一代大模型GPT-5，而是继续训练GPT-4.5，这是当前领先产品的更高效版本。
That could give deep-pocketed rivals such as Google a chance to catch up.
这可能会让谷歌等财大气粗的竞争对手有机会迎头赶上。
Gemini, the tech giant’s soon-to-be-released cutting-edge model, is thought to be more powerful than OpenAI’s current version.
谷歌即将发布Gemini尖端模型，据称这个模型比 OpenAI 当前版本更强大。
[Paragraph 4]
High computing costs have also encouraged the proliferation of much smaller models, which are trained on specific data to do specific things.
高昂的计算成本也促进更小型模型大量涌现，这些模型根据特定数据进行训练，以完成特定任务。
Replit, a startup, has trained a model on computer code to help developers write programs, for instance.
例如，初创公司 Replit 已经用计算机代码训练了一个模型，以帮助开发人员编写程序。
Open-source models are also making it easier for people and companies to plunge into the world of generative AI.
开源模型也让人们和公司更容易参与生成人工智能领域。
According to a count maintained by Hugging Face, an AI firm, roughly 1,500 versions of such fine-tuned models exist.
根据人工智能公司 Hugging Face 的统计，目前大约有1500个版本的此类微调模型。
[Paragraph 5]
All these models are now scrambling for data—the second force shaping the generative-AI industry.
所有这些模型现在都在争夺数据——这是塑造生成式人工智能产业的第二股力量。
The biggest, such as OpenAI’s and Google’s, are gluttonous: they are trained on more than 1trn words, the equivalent of over 250 English-language Wikipedias. As they grow bigger they will get hungrier. But the internet is close to being exhausted.
最大的模型，如OpenAI和Google的模型，是贪婪的：它们的训练数据超过1万亿个词，相当于250多个英语维基百科。随着模型变得越来越庞大，数据需求也会越来越大。然而，互联网数据即将用完。
Many model-makers are therefore signing deals with news and photography agencies.
因此，许多模型制造商正在与新闻和摄影机构签订协议。
Others are racing to create “synthetic” training data using algorithms; still others are trying to work with new forms of data, such as video.
一些制造商正在争分夺秒地利用算法创建 "合成 "训练数据；还有一些制造商正在尝试使用视频等新式数据。
The prize is a model that beats the rivals.
最终目标是研发出能够超越竞争对手的模型。
[Paragraph 6]
Generative AI’s hunger for data and power makes a third ingredient more important still: money.
生成式人工智能对数据和算力的渴求使得第三股力量变得更加重要：金钱。
Many model-makers are already turning away from ChatGPT-style bots for the general public, and looking instead to fee-paying businesses.
许多模型制作商已经开始放弃面向大众的 ChatGPT 式机器人，转而将目光投向付费企业。
OpenAI, which started life in 2015 as a non-profit venture, has been especially energetic in this regard.
OpenAI在2015 年以非营利企业的身份成立，现在在付费这方面表现得尤为积极。
It has not just licensed its models to Microsoft, but is setting up bespoke tools for companies including Morgan Stanley and Salesforce.
它不仅将其模型授权给微软，还为摩根士丹利和 Salesforce 等公司提供定制工具。
Abu Dhabi plans to establish a company to help commercialise applications of Falcon, its open-source AI model.
阿布扎比计划成立一家公司，来帮助其开源人工智能模型 Falcon 的应用商业化。
[Paragraph 7]
Another approach is to appeal to software developers, in the hope of getting them addicted to your model and creating the network effects that are so prized in tech.
另一种方法是吸引软件开发者，让他们沉迷于你的模型，并创造出科技界所珍视的网络效应。
OpenAI is offering tools to help developers build products using its models; Meta hopes that LLaMA, its open-source model, will help create a loyal community of programmers.
OpenAI 正在提供工具来帮助开发者使用其模型构建产品；Meta 希望它的开源模型 LLaMA 能够帮助创建一个忠诚的程序员社区。
[Paragraph 8]
Who will emerge victorious?
谁将胜出？
Firms like OpenAI, with its vast number of users, and Google, with its deep pockets, have a clear early advantage.
拥有大量用户的 OpenAI 和财大气粗的谷歌等公司具有明显的先发优势。
But for as long as computing power and data remain constraints, the rewards for clever ways around them will be large.
但是，只要计算能力和数据仍然是制约因素，那么找到巧妙解决方案就会获得丰厚回报。
A model-builder with the most efficient approach, the most ingenious method to synthesise data or the most appealing pitch to customers could yet steal the lead.
拥有最高效的方法、最巧妙的数据合成方法、或最吸引客户的宣传语的模型创建者可能会抢占先机。
The hype may have cooled. But the drama is just beginning.
炒作可能已经降温，但一场大戏才刚刚开始。
（恭喜读完，本篇英语词汇量671左右）
原文出自：2023年9月23日《The Economist》Leaders版块
精读笔记来源于：自由英语之路
本文翻译整理: Irene
本文编辑校对: Irene
仅供个人英语学习交流使用。
【补充资料】（来自于网络）
网络效应Network Effect是指产品或服务的价值随着用户数量的增加而增加。简单来说，网络效应就是“用户越多，价值越高”。例如，社交媒体平台的价值取决于其用户数量的增加，越多的用户使用该平台，提供的交流、社交和信息共享的价值越高。又如，智能手机的价值不仅取决于其自身的功能和性能，还取决于可用的应用程序和服务。应用程序和服务的开发者和供应商越多，用户就有更多的选择和功能，进一步增加了智能手机的价值。
【重点句子】（3个）
The craze for consumer experimentation has since cooled a little: ChatGPT use has fallen and fewer people are Googling “AI”.
此后，消费者的尝试热潮略有降温：ChatGPT 的使用量下降了，谷歌上“AI”的搜索量也下降了。
High computing costs have also encouraged the proliferation of much smaller models, which are trained on specific data to do specific things.
高昂的计算成本也促进更小型模型大量涌现，这些模型根据特定数据进行训练，以完成特定任务。
Generative AI’s hunger for data and power makes a third ingredient more important still: money.
生成式人工智能对数据和算力的渴求使得第三股力量变得更加重要：金钱。