CORRECTED LINKS: Reid Hoffman on How AI Might Answer Our Biggest Questions

We're resending today's email about our new episode of How Do You Use ChatGPT? with Reid Hoffman with corrected links. Watch on X or YouTube, or listen on Spotify or Apple Podcasts. We sincerely regret the error. —Kate Lee

Reid is the cofounder of LinkedIn, a partner at venture capital firm Greylock Partners, the host of the Masters of Scale podcast, and a prolific author. But before he did any of these things, Reid studied philosophy—and by helping him understand how to think, it made him a better entrepreneur.

A good student of philosophy rigorously engages with questions about truth, human nature, and the meaning of life, and, over time, learns how to think clearly about the big picture. This is a powerful tool for founders faced with existential questions about their product, consumers, and competitors, and enables them to respond with well-reasoned answers and enviable clarity of thought.

This show is usually about the actionable ways in which people have incorporated ChatGPT into their lives, but in this episode, I sat down with Reid to tackle a deeper question: How is AI changing what it means to be human? How might it change the way we see ourselves and the world around us?

This episode is a must-watch for anyone curious about some of the bigger questions prompted by the rapid development of AI. Here’s a taste:

• Study philosophy to be a better founder. Reid believes that philosophy is invaluable for entrepreneurs because it trains them to think about key questions they will encounter while building a business, like “how human beings are now” and “how they are as…the ecosystems we live in change.” His contrarian take is that “a background in philosophy is more important for entrepreneurship than an MBA.”
• Broaden the horizons of what you know. Even outside of business school, Reid thinks philosophy is foundational to other areas of study like economics, game theory, and political science, and believes there are deep benefits in interdisciplinary thinking. “[S]ome of the most interesting people are [those] who are actually blending across disciplines within academia,” he explains.

I asked Reid how LLMs weigh into the long-standing debate between essentialism and nominalism, the two schools of thought that broadly divide the history of philosophy. Before we dive into the details, let me give you some context about my question. 

To begin with, here are a few pointers about LLMs that are relevant to our discussion:

• Natural language processing (NLP) is a field of AI that focuses on helping computers understand human language. It's like teaching a computer to read and comprehend words the way you and I do.
• Embeddings are a technique used in NLP where qualitative data, like words or phrases, are converted into a language that computers can understand, like numbers. To understand how this is done, imagine mapping the qualitative data in a space in a way that preserves as much of the context and meaning of the original data as possible. For instance, words that appear in similar contexts, like “apple” and “orange,” will be closer together than words that are unrelated, like “apple” and “king.” This proximity is what gives embeddings their power, as algorithms can now perform mathematical operations on words, essentially treating them like numbers.
• Next-token prediction is a process where, after learning from lots of text, the LLM tries to guess the next word in a sentence. It's similar to when you're texting and your phone suggests the next word you might want to type. The model calculates the probabilities of many possible next words and chooses the most likely one.

Here’s a brief primer on the concepts of philosophy in the context of which we discuss LLMs and the way they function: