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

**Elad Gil** (0:05)
2025 has been another remarkable year in AI. This week on No Priors, we're sharing our favorite moments from the podcast from the year so far. We've talked to visionary leaders at Harvey, OpenAI, Glean, Abridge, and more. We also talked to legends of science, like Dr. Fei-Fei Li and Noubar Afeyan. But first, let's start with a moment that captures the magic of leaning into new capabilities at the right time. Harvey CEO Winston Weinberg discovered an extraordinary opportunity hidden in plain sight.

**Winston Weinberg** (0:30)
Gabe and I actually had met a couple years before, and I definitely didn't know anything about the start-up world and didn't have a plan of doing a start-up. And what had happened was he showed me GPT-3, which at the time was public. And I was, first of all, just incredibly surprised that no one was talking about GPT-3 and no one was using it in any way, shape or form. And he showed me that, and I showed him kind of my legal workflows. And we started the kind of aha moment was we went on r slash legal advice, which is basically a subreddit where people ask a bunch of legal questions. And almost every single answer is, so who do I sue? Almost every single time. And we took about 100 landlord-tenant questions. And we came up with kind of some chain of thought prompts. And this is before anyone was talking about chain of thought or anything like that. And we applied it to those landlord-tenant questions. And we gave it to three landlord-tenant attorneys. And we just said nothing about AI. We just said, here is a question that a potential client asked. And here is an answer. Would you send this answer without any edits to that client? Would you be fine with that? You know, is that ethical? Is it a good enough answer to send? And 86 out of 100 was yes. And actually, we cold emailed the General Counsel of OpenAI. And we sent him these results. And his response basically was, oh, I had no idea the models were this good at legal.
And we met with the C-suite of OpenAI a couple of weeks after.

**Elad Gil** (2:01)
Now, from legal reasoning to spatial intelligence, the legendary Dr. Fei-Fei Li opened our eyes to an entirely different dimension of AI capability.

**Fei-Fei Li** (2:10)
I think from a neural and cognitive science point of view, that spatial intelligence is a really hard problem that evolution has to solve for animals. And what's really interesting is, I think animals have solved it to an extent, but not fully solved it. It's one of the hardest problems because what is the problem animal has to solve? Animals have to evolve the capability of collecting lights in something which we call eyes mostly. And then with that collection of eyes, it has to reconstruct a 3D world in their mind, somehow, so that they can navigate and they can do things. And of course, they can interact. For humans, we're the most capable animal in terms of manipulation. We can do a lot of things. And all this is spatial intelligence. To me, that's just rooted in our intelligence. What is interesting is, it's not a fully solved problem, even in animals. We, for example, for humans, right? If I ask you to close your eyes right now and draw out or build a 3D model of the environment around you, it's not that easy. We don't have that much capability to generate extremely complicated 3D model till we get trained. You know, there are some of us, whether they're archetypes or designers or just people with a lot of training and a lot of talent. And that's a hard thing to do. And imagine you do it at your fingertip much more easily and allow much more fluid interactivity and editability. That would just be a whole different world for people, no pun intended.

**Elad Gil** (4:13)
Data is the beast feeding the AI train, and that's Mercor CEO, Brendan Foody, is working with major AI labs on how to build what's next. He gives a clear prediction about what's coming for the workforce.

**Brendan Foody** (4:24)
I think displacement in a lot of roles is going to happen very quickly, and it's going to be very painful and a large political problem. Like I think we're going to have a big populist movement around this and all the displacement that's going to happen. But one of the most important problems in the economy is figuring out how to respond to that, right? Like how do we figure out what everyone who's working in customer support or recruiting should be doing in a few years? How do we reallocate wealth once we have, once we approach super intelligence, especially if the value and gains of that are more of a power law distribution? And so I spend a lot of time thinking about how that's going to play out. And I think it's really at the heart of it.