Busy executives and academics are deploying artificial intelligence clones to handle routine communications and calendar demands, according to a New York Times report on the emerging "AI twin" trend.
The practice reflects how white-collar professionals address bandwidth constraints. An AI version of a CEO or professor fields incoming questions, responds to emails, and even attends lower-stakes meetings, freeing the human original for higher-value work. The technology learns from past communications, speech patterns, and decision-making frameworks to mimic the person's voice and judgment.
Companies building these tools include EverytimeAI and others targeting the $2 trillion knowledge worker productivity market. The pitch is straightforward: delegate the repetitive parts of leadership.
This raises immediate questions about corporate liability and authenticity. When an AI version commits to a deadline or misrepresents a position in a meeting, who bears responsibility? Legal frameworks have not caught up. Investment firms and boards may also grow skeptical of leaders who outsource even initial engagement with their organizations.
The broader pattern is clear: companies continue investing heavily in AI to automate white-collar work previously thought resistant to automation. Harvard professors adopting AI twins signals that even elite knowledge workers see concrete value in the technology today, not in some distant future.
The trend also reflects deepening inequality. Only senior executives and well-funded institutions can afford bespoke AI versions. This creates a two-tier workplace where C-suite professionals delegate while junior staff remain subject to traditional performance metrics and meeting loads.
Adoption rates will depend on whether boards and stakeholders accept the practice. Some organizations may view AI twins as cost-cutting theater rather than genuine productivity gains. Others will see them as table stakes in a competitive talent market where top executives demand tools to protect their time.
The technology itself is not novel. Chatbots, digital assistants, and conversational AI have existed for years. What is new is the willingness of recognizable professionals to make the delegation public and to trust AI with their external-facing identity at scale.
Success requires AI systems that genuinely capture individual decision-making patterns rather than default to generic responses. That threshold determines whether these tools become standard productivity infrastructure or a short-lived executive fad.