The Best of 2025 (So Far) with Sarah Guo and Elad Gil

Gabe and I had met a couple of years prior. I knew nothing about the startup world and had no plans to start a company. He showed me GPT-3, which was public at the time. I was incredibly surprised that no one was discussing or utilizing GPT-3 in any capacity. He demonstrated its capabilities, and I, in turn, showed him my legal workflows. The "aha" moment occurred when we visited r/legaladvice, a subreddit where people ask legal questions, and the common answer is, "So, who do I sue?" We took about 100 landlord-tenant questions and developed a series of chain-of-thought prompts. This was before such concepts were widely discussed. We applied these prompts to the questions and presented the results to three landlord-tenant attorneys. Without mentioning AI, we simply asked, "Here's a question from a potential client and an answer. Would you send this answer without edits? Is it ethical and sufficient?" Eighty-six out of 100 attorneys said yes. We then cold-emailed OpenAI's General Counsel with these results. His response was, "Oh, I had no idea the models were this good at legal." We met with OpenAI's C-suite a couple of weeks later.

From a neural and cognitive science perspective, spatial intelligence is a challenging problem that evolution must solve for animals. Animals have largely solved it, but not entirely. It's one of the hardest problems because animals must evolve the ability to collect light, primarily through eyes. With this visual input, they then reconstruct a 3D world in their minds to navigate, perform actions, and interact. Humans, being the most capable animals in terms of manipulation, can accomplish many tasks. All of this relies on spatial intelligence, which is fundamental to our overall intelligence. Interestingly, it remains an incompletely solved problem, even in animals.

I believe significant job displacement will occur rapidly, causing considerable pain and a major political challenge. We'll likely see a large populist movement emerge due to this displacement. A crucial economic problem is determining how to respond. How do we re-skill individuals currently in customer support or recruiting roles for future jobs? How do we reallocate wealth as we approach superintelligence, especially if its value and gains follow a power law distribution? I spend a lot of time contemplating how this will unfold, and I think it's really at the core of…

When writing code, an LLM might attempt to figure things out, but this would require many attempts and self-verification. A simple program could achieve the same results in a verifiable and much faster way. For example, if I'm researching a company and need a valuation model, I could have the LLM try to process that and fit coefficients within its context. Alternatively, I could simply have it write the code to perform the task correctly and provide the actual answer. I believe this approach allows for more efficient compute allocation. You can defer tasks where the model lacks a comparative advantage to a tool better suited for that specific function.