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Harvard’s $699 startup bootcamp has professors who never sleep–but that’s because they’re AI clones

Founders can rehearse pitches, sales calls and board meetings repeatedly with avatars modeled on seven HBS faculty members

Harvard’s $699 startup bootcamp has professors who never sleep–but that’s because they’re AI clones

Harvard Business School is offering an online startup bootcamp for aspiring entrepreneurs, featuring AI clones of its professors. This eight-week program, part of HBS Foundry, allows participants to rehearse investor pitches, sales calls, and board meetings with AI versions of Harvard instructors. The clones, including seven professors with backgrounds in venture capital, business-model design, board dynamics, and startup strategy, were created through voluntary interviews, recording sessions, and ongoing faculty feedback.

The bootcamp, priced at $699, is designed to give founders another way to prepare and challenge their thinking, so when they meet live with real instructors, more time can be spent on questions where human judgment and conversation matter most. Participants have included 760 founders so far, and the bootcamp culminates with an opportunity to pitch to investors for $100,000.

Foundry's director, Kat Rings, explained that the AI experiences were requested by founders themselves who wanted to practice and be challenged before meeting mentors and investors. However, some university professors have expressed concerns about AI, with some urging universities to prevent AI-enabled cheating on exams. Despite this, other institutions are using AI to scale faculty time into a digital product, such as Imperial Business School's digital twins of professors and Georgia Tech's AI teaching assistant Jill Watson.

The use of AI in education raises questions about what AI can and cannot replicate, such as a professor's ability to recognize potential, inspire a student, and make introductions based on years of human interaction.

Written by urgent.news from Fortune's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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