No Priors Ep. 138 | The Best of 2025 (So Far) with Sarah Guo and Elad Gil

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, A Bridge, and more. We also talked to legends of science like Dr. Fay Feay Lee and Nubar Fayen. 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.

Gabe and I had met a couple of years before, and I definitely didn't know anything about the startup world and didn't have a plan to do a startup. What had happened was he showed me GPT-3, which at the time was public, and I was incredibly surprised that no one was talking about GPT-3 and no one was using it in any way, shape, or form.

He showed me that, and I showed him my legal workflows. The aha moment was when we went on r/legaladvice, which is basically a subreddit where people ask a bunch of legal questions, and almost every single answer is, “Who do I sue?” Almost every single time. We took about 100 landlord-tenant questions and came up with some chain-of-thought prompts. This was before anyone was talking about chain of thought or anything like that.

We applied it to those landlord-tenant questions and gave it to 3 landlord-tenant attorneys. We said nothing about AI. We just said, “Here's 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? Is it ethical? Is it a good enough answer to send?” Eighty-six out of 100 said yes.

We cold-emailed the general counsel of OpenAI and sent him these results. His response was basically, “Oh, I had no idea the models were this good at legal.” We met with the C-suite of OpenAI a couple of weeks after that.

Now, from legal reasoning to spatial intelligence, the legendary Dr. Fay Lee opened our eyes to an entirely different dimension of AI capability.

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. What's really interesting is that I think animals have solved it to an extent, but haven't fully solved it. It's one of the hardest problems, because what is the problem an animal has to solve?

Animals have to evolve the capability of collecting light in something we call eyes, mostly. Then, with that collection of light, they have to reconstruct a 3D world in their mind somehow so that they can navigate, do things, and, of course, interact.

For humans, we're the most capable animal in terms of manipulation, and we can do a lot of things. All this is spatial intelligence. To me, that's just rooted in our intelligence. What's interesting is that it's not a fully solved problem even in animals.

For example, for humans, 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 an extremely complicated 3D model until we get trained. There are some of us, whether they're architects or designers or just people with a lot of training and a lot of talent, who can do that.

That's a hard thing to do. Imagine you do it at your fingertips much more easily and allow much more fluid interactivity and editability. That would just be a whole different world for people. No pun intended.

Data is the beast feeding the AI train. And thus, Merkore 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.

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. 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. 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 approach superintelligence, especially if the value and gains of that are more of a power-law distribution?

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—

What do you think happens eventually? X% of people get displaced from white-collar work. What do you think they do?

I think there's going to be a lot more in the physical world. I think there's also going to be a lot that's niche.

What does the physical world mean?

Well, it could be everything ranging from people who are creating robotics data to people who are waiters at restaurants, or who are just therapists, because people want human interaction—whatever that looks like.

I think automation in the physical world is going to happen a lot slower than what's happening in the digital world, just because of so many of the self-reinforcing gains and a lot of self-improvement that can happen in the virtual world but not the physical one.

Which brings us to one of the biggest questions of our time. How do we navigate the geopolitical implications of super intelligence? Dan Hendris, the director of the Center for AI Safety, has an answer.

Let's think of what happened in nuclear strategy. Basically, a lot of states deterred each other from doing a first strike because they could then retaliate. So they had a shared vulnerability. They were saying, “We're not going to do this really aggressive action of trying to make a bid to wipe you out, because that will end up causing us to be damaged.”

We have a somewhat similar situation later on, when AI is more salient, when it is viewed as pivotal to the future of a nation, and when people are on the verge of making a superintelligence—when they can automate pretty much all AI research.

I think states would try to deter each other from trying to leverage that to develop it into something like a superweapon that would allow the other countries to be crushed, or to use those AIs to do some really rapid, automated AI research-and-development loop that could bootstrap from its current levels to something that's superintelligent, vastly more capable than any other system out there.

I think later on it becomes so destabilizing that China just says, “We're going to do something preemptive, like a cyberattack on your data center,” and the U.S. might do that to China.

Russia, coming out of Ukraine, will reassess the situation, get situationally aware, and think, “Oh, what's going on with the U.S. and China? Oh, my goodness, they're so far ahead on AI. AI is looking like a big deal.”

Let's say it's later in the year, when a big chunk of software engineering is starting to be impacted by AI: “Oh, wow, this is looking pretty relevant. Hey, if you try and use this to crush us, we will prevent that by doing a cyberattack on you, and we will keep tabs on your projects because it's pretty easy for them to do that espionage.”

The motivation for Flagship stems from what I was doing before, which was that I started a company in 1987, when 24-year-old immigrants didn't start companies in this country. Instead, former Merck senior executives or IBM senior executives were the only ones who were entrusted with the massive amounts of venture capital—namely, $23 million per round—that used to go into venture capital.

This was very early days, and I had the opportunity to start a company right out of graduate school. I ended up raising quite a bit of venture money and eventually went down a path of entrepreneurship.

One of the things that interested me was why the entrepreneurial process was supposed to be random, improvisational, idiosyncratic, almost emotional, and gamey. All of those things I thought were a bit of a put-off when it comes to actually doing things in a serious, professional way.

I used to go around in the very early ’90s saying, “Why isn't entrepreneurship a profession?” And if it was going to be a profession, how could it be a profession?

What do you mean by “gamey”?

Because it's supposed to fail most of the time, and once in a while you win and then you celebrate the win. What I mean is, it's random.

It's random.

But not only random—there are winners and losers and keeping score. I don't know. It's maybe the wrong word, but people even call it gamification in the software space. There's a version of this.

I don't mind being playful, because if you're overly serious, sometimes you miss things. But it can't just all be play. We take hard-earned money. We deploy it to do things that are damn near impossible. Once in a while, we reduce them to practice so they become not only possible but valuable.

And yet people treat it like, “Oh, well, it didn't work. There are 20 different things we tried. One of them worked.” And that, I don't know—as an engineer by background, as a scientist—I just thought that what we do, especially in healthcare, especially in climate, especially in agriculture and food security, you can't think of this as shots on goal. You've got to say, “Hey, we can get better at this.”

Reasoning is the biggest paradigm shift in AI architecture since the transformer. Brandon McKenzie and Eric Mitchell from OpenAI explained a crucial insight about reasoning models.

I can give very concrete cases for the visual reasoning side of things.

There are a lot of cases where the model can estimate its own uncertainty. You'll give it some kind of question about an image, and the model will very transparently tell you in a thought, like, “I don't know. I can't really see the thing you're talking about very well.” It almost knows that its vision is not very good.

But what's kind of magical is that when you give it access to a tool, it's like, “Okay, well, I've got to figure something out. Let's see if I can manipulate the image or crop around here,” or something like this. What that means is that it's a much more productive use of tokens as it's doing that. Your test-time scaling slope goes from something like this to something much steeper.

We've seen exactly that: the test-time scaling slopes without tool use and with tool use, specifically for visual reasoning, are very noticeably different.

Yeah. I also say, for writing code, there are a lot of things that an LLM could try to figure out on its own but would require a lot of attempts and self-verification that you could write a very simple program to do in a verifiable and much faster way.

You could say, “Hey, do some research on this company and use this type of valuation model to tell me what the valuation should be.” You could have the model try to crank through that and fit those coefficients or whatever in its context, or you could literally just have it write the code to do it the right way and know what the actual answer is.

I think part of this is that you can allocate compute a lot more efficiently because you can defer things that the model doesn't have a comparative advantage in doing to a tool that is really well suited to doing that thing.

Sometimes the most profound moments in AI development aren't the grand theoretical breakthroughs. They're based on taste, data generation, and grinding work—the visceral experience of watching something you hoped would work actually come to life. Isa Fulford from OpenAI captures that moment perfectly. Here she's describing the training that went into Deep Research.

It really was one of those things where we thought that training on browsing tasks would work. It felt like we had good conviction in it. But actually, the first time you train a model on a new data set using this algorithm and see it actually working and play with the model was pretty incredible, even though we thought it would work.

Honestly, just that it worked so well was pretty surprising.

Mm-hmm.

Even though we thought it would, if that makes sense.

Yeah. It's the visceral experience of, like, “Oh, the path is paved with strawberries,” or whatever.

Exactly. But then sometimes some of the things that it fails at are also surprising. Sometimes it will make a mistake where it will do such smart things, and then make a mistake where I'm just thinking, “Why are you doing that? Stop.”

So I think there's definitely a lot of room for improvement. We've been impressed with the model so far.

One of the biggest surprises of AI, and a core principle for us here at Conviction, is how it can make bad markets suddenly good ones. The right technology can meet the right moment in unexpected ways. Arvind Jain built Glean in what everyone said was a graveyard market: enterprise search.

It was like a graveyard, with all these companies that tried to solve the problem and didn't. Part of it was just that search is a hard problem in an enterprise. Even getting access to all the data that you want to search was such a big problem in the pre-SaaS world. There was no way to go into those data centers, figure out where the servers were and where the storage systems were, and try to connect with the information in them. It was a big challenge.

SaaS actually solved that issue. Most search products and most of the companies started in the pre-SaaS world. They failed because you just couldn't build a turnkey product. But SaaS actually allowed you to build something.

My insight was that the enterprise world had changed. We had these SaaS systems now, and SaaS systems don't have versions—everybody, all customers, have the same version. They're open, they're interoperable, and you can actually hit them with APIs and get all the content.

I felt that the biggest problem was actually solved: I could easily go and bring all the enterprise information and data into one place and build this unified search system on top. That was a big unlock.

By the way, the origins of Glean go back to Rubrik. We had this problem: We grew fast, we had a lot of information across 300 different SaaS systems, and nobody could find anything in the company. People were complaining about it in our pulse surveys. I always ran those in my startups, and this was a complaint that came to me. I had to solve it.

I tried to buy a search product, and I realized there was nothing to buy. That's really the origin of how Glean got started as a company. SaaS made it easy to connect your enterprise data and knowledge to a search system. That made it possible for us, for the very first time, to build a turnkey product.

But there are a lot of other advances as well. Businesses have so much information and data. One interesting fact is that one of our largest customers has more than 1 billion documents inside their company.

When Larry and I were working on search at Google in 2004, the entire internet actually had 1 billion documents. There's been a massive explosion of content inside businesses. You have to build scalable systems, and you couldn't build a system like that before, in the pre-cloud era.

Perhaps no story captures the human impact of this AI moment and its potential better than what's happening in healthcare. Here's Shiv Rao, CEO and founder of Abridge.

It's pretty heroic, in general, for a doctor to give you feedback like, “Hey, this sucked, and you've got to do better. You didn't recognize the way I said this medication,” or, “I'm a gastroenterologist, and I would never sequence my problems in my assessment and plan section of my note this way. It doesn't serve me well and makes me look terrible as a doctor,” or whatever.

We get that feedback. We love it. It's oxygen. But then we also get feedback like, “Hey, this is amazing, and I'm not going to retire anymore. I've got years, decades left in my career now, thanks to this technology.”

In this love-stories channel, all of that feedback, that positive feedback, gets programmatically funneled. Any one of our people inside the company can always go into that channel, and its purpose. It's fulfillment, immediately. You immediately understand why we're all working so hard and why it makes sense.

Being on this very entrepreneurial journey these last couple of years is obviously new for so many of us. We're all kind of building new muscles, but it's a lot of pressure. This is my favorite bit of feedback.

This love story comes from a doctor at Tanner Health, which is a rural health system. She wrote to us:

“I was sitting at dinner last week, and my son asked me, ‘Mommy, why aren't you working right now?’ I literally took my phone out and explained to him that Abridge is a new tool that lets Mommy come home early and eat dinner with her family.”

I started to tear up and looked over at my husband, who then said, “Mommy's going to be able to eat dinner with us every night now.”

We get feedback like that every day. There are dopamine hits in hypergrowth, and those are awesome, but I think they get us through sprints. I think it's the oxytocin hits like this. It's the purpose. It's the fulfillment. That's, I think, what we're really after in this company.

Everybody's mission-driven out there, but I think this mission hits me at least a little bit differently.

These conversations remind us that we're living through a hinge moment in history. Stay tuned as we have more conversations with the builders and thinkers leading the way for the rest of the year. If you like what we're doing, leave us a review on Apple Podcasts or Spotify, comment on YouTube, or let us know who we should have as a guest. Thanks for listening. Find us on Twitter at no prior pod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcast, Spotify, or wherever you listen. That way, you get a new episode every week. And sign up for emails or find transcripts for every episode at no-briers.com.