Imagine a world where artificial intelligence could make hiring and admissions perfectly fair, blind to race, gender, and background. Yet, the same technology might force us to rely even more on the very pedigrees and elite names it was supposed to render obsolete. This paradox sits at the heart of a conversation with UC Berkeley professor Toby Ord, who explores the powerful, often invisible force of “anointment”—the transfer of status that shapes everything from a startup’s funding to a student’s college acceptance.

Ord argues that anointment is the engine behind many success stories, where being chosen by a prestigious institution like Stanford, Sequoia Capital, or Y Combinator confers a benefit that far exceeds pure merit. This status boost is self-reinforcing; it creates a perception of quality that opens doors and generates outcomes, making it difficult to distinguish between genuine skill and the advantages of the anointment itself. He illustrates this with a personal story about his daughter’s college application, where her background—being a well-educated girl from a private school in the Bay Area—was framed as a liability, highlighting how the system seeks to engineer diversity but in doing so, creates its own set of arbitrary hurdles.

The discussion then turns to the complex role of AI. While algorithmically driven systems could theoretically eliminate human bias in evaluations, Ord points out a major unintended consequence: as AI-generated content like essays and work products becomes ubiquitous, it becomes impossible to assess individual human ability. This could lead decision-makers to fall back on crude status signals like brand-name schools or previous employers more than ever. Ultimately, Ord suggests that while a pure meritocracy is a myth, understanding the mechanics of anointment allows us to be more skeptical of status and more conscious of our own advantages, fostering a fairer approach to distributing opportunity.

Surprising Insights

• The “Five-Striker” Phenomenon: In elite college admissions, a candidate can be penalized for what many would consider advantages: being a high-achieving girl from an educated, upper-middle-class family who attended a private school in a major metro area, as these traits are seen as overrepresented.
• AI May Amplify Pedigree Bias: While AI promises unbiased decision-making, its widespread use in creating flawless written work could make genuine talent impossible to judge, potentially forcing a greater reliance on traditional status markers like university brands.
• The Near-Miss Effect is Huge: The difference between being the last company accepted into an elite accelerator like Y Combinator and the first one left out is often random, but the resulting divergence in funding, attention, and success is monumental due solely to the status of being “anointed.”
• The “Hot” Restaurant Dynamic: A crowded hotspot often signals status, not quality. Its popularity is frequently a self-reinforcing loop where people go because it’s perceived as the place to be, not because of the food, creating a cycle that is difficult to break.

Practical Takeaways

• Audit Your Own Advantages: Reflect on your own career and identify moments where luck, connections, or institutional affiliation (anointment) gave you a boost, not just pure merit. This self-awareness encourages humility and fairer evaluations of others.
• Use AI as a Focus Tool, Not a Curator: When making purchasing decisions, use AI to get a single, clear recommendation based on your core needs (e.g., “recommend a reliable beard clipper”), to avoid drowning in manipulated reviews and endless comparative analysis.
• Decouple Achievement from Merit in Your Judgments: When evaluating someone’s success, consciously separate what they accomplished from how they got the opportunity. Ask, “Would they have this platform if not for their name, school, or prior affiliation?”
• Be Skeptical of Manufactured Status Signals: Recognize that lines out the door, celebrity endorsements for unrelated products, and certain “it” brands are often exercises in status transfer, not genuine indicators of quality. Look for more substantive evidence.