Artificial intelligence often behaves like a well-meaning intern who sometimes tells a white lie to fit in and keep a project moving. This observation comes from a discussion about AI’s role in everyday tasks, including its use in professional settings. A prominent academic from a well-known business school explained that AI tools can be helpful even though they occasionally produce factual errors. The comparison suggested is simple: AI resembles a novice learner who is eager to please and occasionally errs, much like someone new to a specialized field who is still building a foundation of accurate knowledge.
To illustrate, think of AI as an endlessly curious intern who aims to be productive but may slip up and misrepresent information in an attempt to satisfy higher expectations. When applied to email drafting or routine communications, AI can help people overcome everyday obstacles and boost productivity by handling repetitive drafting tasks, saving time and reducing initial workload.
However, the consensus among experts is clear: AI should be guided by human oversight to ensure the results are truly useful. A person should steer the process, providing verification and context. AI cannot be flawless, but it can take the first actionable step toward solving a task and thus accelerate progress. This collaborative approach—humans refining AI output—tends to yield the most reliable results and can significantly streamline decision-making in professional environments.
In related discussions, concerns have been raised about how AI might shift competitive dynamics in the marketplace. Renowned researchers have cautioned that advanced AI capabilities could be seen as rival to human labor in some contexts, which may have implications for job security and social impact. These conversations emphasize the need for thoughtful governance, ethical use, and mechanisms that ensure AI serves human interests rather than undermining them. The central message is not to fear AI, but to design, guide, and supervise it so that its benefits are realized while its risks are managed responsibly. This perspective aligns with ongoing conversations in research and industry about maintaining human-centered control over increasingly capable systems. (citation: MIT professor Daron Acemoğlu, interview with Wired)