Wedoany.com Report on Feb 25th, In a recent podcast interview, Uber CEO Dara Khosrowshahi revealed that an internal team of engineers at the company has developed an AI version of himself, specifically designed to help employees rehearse their presentations before the official reporting. This detail was first reported by Business Insider and subsequently garnered attention within the tech community.
During an appearance on Steven Bartlett's podcast, "The Diary of a CEO," Khosrowshahi stated: "One of my team members told me that some teams have developed a Dara AI, so they can basically present to Dara AI as preparation for presenting to me." He explained that since the final presentation materials he sees are typically polished through multiple layers of refinement, employees can use this AI counterpart to simulate feedback in advance, optimizing their reporting strategy and content structure.
The development of this internal tool was not an official project but rather a spontaneous efficiency innovation by the engineering team. Khosrowshahi expressed approval for the initiative and further shared the current widespread application of AI within Uber. He noted that approximately 90% of Uber's software engineers use AI technology in their daily work, with about 30% being "heavy users." These engineers are leveraging AI to rethink the architectural design of the company's core systems.
Khosrowshahi described it as: "They are making the bricks that go into the system, like architects thinking about what the system should look like." He believes the use of AI tools has significantly enhanced team productivity, especially in areas such as code generation, system optimization, and cross-departmental collaboration. "It has truly changed their productivity in a way I've never seen before," he said.
The emergence of Uber's internal AI counterpart showcases new possibilities for generative AI in enterprise scenarios: from simulating management feedback to reconstructing technical architectures, AI is gradually permeating the deeper layers of decision-making processes and organizational collaboration. This case also provides a reference model for other companies exploring AI-driven internal efficiency tools.
