ReadingResearchRadarInvestment frameworkAI Company Primary Sources
Sign in / Sign up
← Back to reading
NewsMaterial3 min readDisconfirmAI Editorial

Berkshire Partners Investor Richard Lichtenstein: AI Assistants May Need a Digital Twin

Private equity investor Richard Lichtenstein proposes an exploratory idea in his personal blog: AI assistants like Muse, Instinct, and Dots may require a complete digital twin to better handle business tasks autonomously. He illustrates this concept using the analogy of virtual simulation in BMW factories, but notes a key limitation—corporate documents record prescribed processes rather than actual operations, with significant information still residing in people's minds.

2026-10-29 18:03 GMT+8

Richard Lichtenstein is an investor at Berkshire Partners. In a personal blog post dated October 29, 2026, he proposed an exploratory idea: for AI assistants like Muse, Instinct, and Dots to better handle business tasks autonomously, he believes it is time to build them a digital twin. His exact words were:

"I've been thinking about what would make these tools better, and I think what they really need is a good digital twin."

The tone is one of personal reflection rather than a definitive conclusion. His basic logic is that if AI assistants have a deeper understanding of user preferences and organizational workflows, they can make more decisions with less human intervention.

He borrows the concept of industrial digital twins to explain this approach. BMW has created digital replicas of more than thirty of its plants; before launching new car models, they test on virtual production lines whether components will collide, compressing physical testing time from four weeks to three days. His analogy suggests that if we built a digital replica of a person—a single physical scan enabling consistent sizing for online shopping and pre-hemmed pants based on leg length—return rates would decrease significantly.

However, he acknowledges that this consumer-focused version is impractical, shifting instead to a more feasible approach: creating a mental digital twin for individuals. He gives the example that if he provided an AI with the itineraries of his last five family trips along with frank summaries of what worked and what didn't, he bets it could plan the next trip effectively. Yet he points out this requires considerable upfront investment and may take several trips to pay off. Currently, these assistants lack mechanisms to proactively seek feedback—he booked a restaurant via Instinct, which never asked about his experience, a missed opportunity he identifies.

Sign in / Sign up
ReadingResearchRadarInvestment frameworkAI Company Primary Sources

The article's focus shifts to the business version. Lichtenstein argues that the corporate side may be easier to implement because more ideas are already documented. He envisions a scenario where, with an exact mirror of a company, you could instruct an AI assistant to monitor the top thirty suppliers, automatically developing backup plans and contacting alternative suppliers upon negative news; follow up with delinquent customers while noting special terms and historical exemptions; identify overdue contacts and reinitiate communication.

For private equity investors, he proposes a specific application: upon entering a newly acquired portfolio company, test parts of the value creation plan on the digital twin before executing them in the real business.

However, he provides the most critical caveat in the article: connecting all documents does not equate to building a digital twin. Documents tell you how a company is supposed to work, not necessarily how it actually works. A large amount of important information remains in people's heads. This means that even if a company stores contracts, SOPs, supplier scorecards, and key meeting presentations in SharePoint or Google Drive, and connects AI to ERP, CRM, email, and instant messaging systems, the result is still an incomplete mirror.

He concludes that enterprise digital twins are easier to achieve than consumer scanning, and the direction is already underway. But the article ends with an open question:

"What do you most want to build a digital twin of?"

Inviting reader comments rather than offering a definitive conclusion. For AI investment research practitioners, the value of this short piece lies in clearly identifying a specific limitation: when assessing whether AI assistants can autonomously handle portfolio company tasks, the convenience of document access cannot replace the acquisition of knowledge about actual operations, which currently resides largely in people's minds and has not yet been captured by any data architecture.

Sources

  • artificialinvestment.substack.com↗
Continue researching →