AI公司已经抓取了大量网络数据。现在他们追逐的东西更有价值：能够展示人类实际工作方式的数据。这是《商业内幕》新系列《AI数据争夺战》的焦点。本周，我们发表了三篇报道，展示职场数据争夺战正在加速。
AI companies have already scraped much of the web. Now they're chasing something even more valuable: data that shows how humans actually work. That's the focus of Business Insider's new series, The AI Data Grab. This week, we published three stories showing how the race for workplace data is accelerating.
其中一篇由查尔斯·罗勒特、汤姆·卡特和斯蒂芬·康瑟尔撰写，考察了公司记录蓬勃发展的市场。
One piece, from Charles Rollet, Tom Carter, and Stephen Council, examines the booming market for company records.
AI公司正向企业提供六位数甚至七位数的报酬，以获取匿名的Slack消息、电子邮件、演示文稿和内部文档。
AI firms are offering businesses six- and even seven-figure sums for anonymized Slack messages, emails, presentations, and internal documents.
其目标是教AI系统人们如何解决问题、协作、决策以及完成长期多步骤任务。这些真实工作流程正成为下一代AI代理的燃料。
The goal is to teach AI systems how people solve problems, collaborate, make decisions, and complete long, multi-step tasks. Those real-world workflows are becoming the fuel for the next generation of AI agents.
另一篇由梅莉亚·罗宾逊撰写的文章探讨了律师事务所如何围绕数十年的法律知识构建AI系统，将自身经验转化为竞争优势，而不仅仅依赖现成的AI模型和工具。
Another story, from Melia Robinson, explores how law firms are building AI systems around decades of legal knowledge, turning their own experience into a competitive advantage rather than relying solely on off-the-shelf AI models and tools.
第三篇由Rya Jetha撰写的文章揭示了AI公司如何支付人们记录真实工作，例如做晚餐和打包仓库，以教机器人人类如何执行日常身体任务。这些报道共同指向AI发展的重大转变。第一波生成式AI主要聚焦于更大的模型和更快的芯片。
A third piece, by Rya Jetha, reveals how AI companies pay people to record real-world work, such as cooking dinners and packing warehouses, to teach robots how humans perform everyday physical tasks. Together, these stories point to a major shift in AI development. The first wave of generative AI was mostly about bigger models and faster chips.
下一章节是关于机器理解工作是如何完成的，这样AI才能帮助我们更好、更快地完成工作。
The next chapter is about machines understanding how work gets done, so AI can help us do it better and quicker. here. Reach out to me via email at abarr@businessinsider.com