Epstein’s Extensive Support for AI Researcher Joscha Bach (Image Credits: Pixabay)
Recently unsealed Department of Justice documents from Jeffrey Epstein’s files shed light on his financial and personal connections to prominent figures in artificial intelligence and brain science research.
Epstein’s Extensive Support for AI Researcher Joscha Bach
Emails in the files detailed how Epstein provided substantial personal funding to Joscha Bach, a German AI expert known for developing cognitive architectures that mimic human thought processes.
Bach pursued postdoctoral studies at MIT during a period when Epstein covered his living expenses in Menlo Park, including rent, travel, healthcare, and his children’s private school fees from 2013 to 2019.
Today, Bach leads the California Institute for Machine Consciousness, an organization exploring the potential for machine sentience. Epstein’s involvement began through mutual contacts in AI and psychology, supporting Bach’s projects at MIT’s Media Lab and Harvard’s Program for Evolutionary Dynamics.
No evidence in the documents suggested any misconduct by Bach, who noted that MIT had approved the funding arrangement.
A Neuroscientist’s Failed Bid for Epstein Funding
Antonio Damasio, director of the Brain and Creativity Institute at USC, approached Epstein in 2013 with a proposal for innovative robotics and neuroscience studies focused on the brain’s emotional origins.
Damasio and a colleague met Epstein at his New York residence to pitch the project, seeking flexible private support to maintain control over their research direction. Epstein declined the request.
Damasio later explained he had been unaware of Epstein’s criminal history and sought a respected donor for work on how emotions underpin decision-making, memory, and social understanding. He emphasized that emotions, rooted in biological vulnerability, could enhance AI systems by fostering creative problem-solving in robots.
Connections to Visionary Computer Scientist David Gelernter
Correspondence between 2009 and 2015 linked Epstein to Yale professor David Gelernter, whose early ideas on “computed worlds” foreshadowed digital twins and immersive virtual environments.
Gelernter, author of Mirror Worlds, once launched a company around these concepts, though it folded after failing to attract widespread adoption. In his exchanges with Epstein, he requested business guidance rather than direct funding.
The files contained no signs of impropriety by Gelernter, who stated he knew nothing of Epstein’s offenses. Gelernter survived a 1993 mail bomb attack by Ted Kaczynski, suffering lasting injuries, and holds outspoken views on academia, gender roles, and climate science.
MIT Pioneer Marvin Minsky and the Media Lab Controversy
Marvin Minsky, a foundational AI figure who helped launch the field at MIT in the 1950s, received Epstein’s support through a $100,000 donation in 2002, part of larger gifts totaling $850,000 to the Media Lab by 2017.
Minsky passed away in 2016, but 2019 court filings from victim Virginia Giuffre alleged involvement in Epstein’s activities, claims his wife refuted as impossible. These disclosures triggered scrutiny at MIT over accepting Epstein’s funds post his 2008 conviction.
The Broader Implications of Private Funding in AI
Epstein’s engagements occurred amid evolving AI research economics, where private donors increasingly filled gaps left by limited government grants, especially as costs for talent and computing soared.
- Private philanthropy offered speed and flexibility over bureaucratic federal awards.
- It demanded less oversight, potentially obscuring donor backgrounds.
- Government priorities shifted toward defense, sidelining exploratory work.
- Recent U.S. cuts to NSF grants, exceeding $1 billion since 2025, heightened reliance on such sources.
Key Takeaways
- Epstein’s funding reached core AI innovators despite his convictions.
- Researchers often lacked full knowledge of his crimes.
- Private money’s rise poses ongoing ethical challenges for tech research.
Epstein’s story underscores the vulnerabilities in AI’s funding landscape, where innovation’s price tag invites shadowy influences. As tech advances accelerate, how can the field safeguard its integrity? Share your thoughts in the comments.
