Philosophy Equips Entrepreneurs Better Than MBA; AI Reshapes Truth

Key Quotes

"You know one of the things that I'll sometimes tell MBA schools when I give talks there is a background in philosophy is more important for entrepreneurship than an MBA. Which of course is is startling and contrarian and part of that is to get people to think crisply about this stuff because part of what you're doing as an entrepreneur is you're thinking about what is the way the world could be, what could it possibly be."

Reid Hoffman argues that a philosophy background is more valuable for entrepreneurship than an MBA because it cultivates crisp thinking about possibilities. This ability to envision potential futures is crucial for entrepreneurs who are constantly exploring new ideas and market opportunities.

"Philosophy is very important to this stuff because it's understanding how to think about very crisply what are possibilities, what are theories of human nature as they are manifest today and as they may be modified by new products and services, new technology etc."

Hoffman emphasizes that philosophy is essential for entrepreneurs because it provides a framework for precisely analyzing potential scenarios and understanding human behavior. This includes considering how new technologies and products might alter existing human nature and societal norms.

"The fundamental problem is is they try to frame it to get to get an intuition to derive an intuition a principle etc. They try to frame an artificially different environment so it's like no no it's a trolley and the trolley will either hit the the the five criminals or the one human baby and it's default set to hit the human baby and do you throw the switch or not."

Hoffman critiques the common use of "trolley problems" as thought experiments, explaining that their fundamental flaw lies in creating artificial environments to elicit specific intuitions. He points out that these scenarios often oversimplify complex ethical dilemmas by presenting limited, unrealistic choices, which can lead to flawed reasoning.

"I think they add perspective and color. I don't think they resolve the debate. And there's certainly some question about since they function more like later Wittgenstein or more, you know, kind of nominalist, you know, you say well does that does that weigh in on the side of nominalists because of actually in fact the way they function."

Hoffman suggests that large language models (LLMs) can offer new perspectives on philosophical debates, such as essentialism versus nominalism, but do not resolve them. He notes that their functional similarity to later Wittgenstein's ideas, which lean towards nominalism, is interesting, but their development also aims for more essentialist characteristics.

"The answer is certainly yes on the relevant ideas. Currently I think we're doing a couple of things. So I think we're we're taking kind of call it, you know, human knowledge and figuring out how to get that as part of what's trained."

Hoffman explains that incorporating human knowledge into AI training is a key strategy for developing reasoning capabilities. He highlights that models trained on diverse data, including computer code and textbooks, demonstrate improved reasoning skills, suggesting that the quality and nature of the training data are crucial for AI development.

"The twistiness of the thinking is one of the things that is that is, you know, was one of the things that made Gödel so spectacular in this. Another one by the way that were historical walks is Einstein and Gödel used to take walks, you know, you wish that you had digital recorders like please record the conversation."

Hoffman expresses a fascination with the complex thought processes of figures like Gödel and Einstein, lamenting the loss of their conversations to history. He suggests that understanding such intricate thinking is vital for advancing fields like AI, and that recording such intellectual exchanges would be invaluable.