No Priors: The Best of 2025 (So Far)

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[Music] 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 RV, OpenAI, Glien, 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.

RV CEO Winston Weinberg discovered an extraordinary opportunity hidden in plain sight.

Gabe and I actually had met a couple years before and I definitely didn’t know anything about the startup world and didn’t have a plan of doing a startup.

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/legaladvice, 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 a hundred 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?

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.

Now, from legal reasoning to spatial intelligence, the legendary Dr. Fei-Fei Li 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.

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 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.

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 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.

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.

Data is the beast feeding the AI train.

And thus, Merc 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 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.

What do you think happens eventually?

X percent of people get displaced from color work.

What do you think they do?

I think there's going to be a lot more of the physical world.

I think that there's also going to be a lot of niche skills.

What does the physical world mean?

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

Whatever that looks like, I think that 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 for school.

Which brings us to one of the biggest questions of our time.

How do we navigate the geopolitical implications of super intelligence?

Dan Hendricks, 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.

They had a shared vulnerability.

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.

When people are on the verge of making a super intelligence more, when they can, say, 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 super weapon that would allow the other countries to be crushed or use those AIs to do some really rapid automated AI research and development loop that could have it bootstrapped from its current levels to something that's super intelligent, vastly more capable than any other system out there.

I think that later on, it becomes sort of stabilizing that China just says we're going to do something preemptive like do a cyber attack on your data center.

And the US might do that to China.

And Russia, coming out of Ukraine, will reassess the situation, get situationally aware, think, "Oh, what's going on with the US and China?

Oh my goodness, they're so head 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 cyber attack on you.

And we will keep tabs on your projects because it's pretty easy for them to do that espionage."

Nubara Fayan has been thinking about how biotech gets built and how to change the game for three decades.

His breakthroughs have impacted global health.

He's the founder and CEO of Flagship Pioneering and the co-founder of Moderna.

He wants to make entrepreneurship a scientific effort, not a random one.

And he thinks AI can help.

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.

But instead it was kind of like former Merck senior executives or IBM senior executives were the only ones who were entrusted with massive amounts of venture capital, namely $2-3 million per round used to go into venture capital.

So this was very early days.

And I had the kind of chance opportunity to start a company right out of my graduate school and ended up raising quite a bit of venture money and eventually kind of went down a path of entrepreneurship.

Along the way, one of the things that interested me was why it is that kind of the entrepreneurial process was supposed to be random improvisational, kind of idiosyncratic, almost emotional, gamey.

All of those things I kind of thought was a bit of a put-off when it comes to actually doing things in a serious professional way.

And I kind of 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 like supposed to fail most of the time and once in a while you win and then you celebrate the win.

And what I mean is like it's random, but not only random, but there's like winners and losers and keeping score.

I don't know, it's maybe the wrong word, but I just mean like people even caught gamification in the software space.

There is a version of this, like I don't mind being playful because if you're overly serious sometimes you miss things, but it can't just all be played.

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, you know it didn't work.

There's 20 different things we tried.

One of them worked.

That, I don't know, as an engineer by background, as a scientist, I just thought that what we do, especially listening in healthcare, especially in climate, especially in agriculture, food security, you can't think of this as like shots on goal and this and that.

You've got to kind of say, hey, we can get better at this.

Reasoning is the biggest paradigm shift in AI architecture since the transformer.

Brandon McKinsey and Eric Mitchell from OpenAI explained a crucial insight about reasoning models.

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

There's a lot of cases where, and back to also the model being able to estimate its own uncertainty, you'll give it some kind of question about an image and the model will very transparently tell you when it should have thought like, I don't know, I can't really see the thing you're talking about very well.

Or like, it almost knows that its vision is not very good.

And well, it's kind of magical.

It's like when you give it access to a tool, it's like, okay, well, I got to figure something out.

Let's see if I can like manipulate the image or crop around here or something like this.

And what that means is that it's like much more productive use of tokens as it's doing that.

And so your test time scaling slope, you know, goes from something like this to something much deeper.

And we've seen exactly that like the test time scaling slopes for without tool use and with tool use for visual reasoning specifically are very noticeably different.

Yeah.

I also say like, for like writing code for something like 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 like a verifiable and, you know, much faster way.

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

And so, yeah, I think like part of this is you can just allocate compute a lot more efficiently because you can defer stuff that the model doesn't have comparative advantage to doing to a tool that is like really well suited to doing anything.

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.

Isla Falford from opening AI 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, you know, training on browsing tasks would work, you know, felt like we had good conviction in it.

But actually, the first time you train a model on a new dataset using this algorithm and seeing it actually working and playing with the model was pretty incredible, even though we thought it would work.

So honestly, just that it worked so well was pretty surprising, 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.

Like sometimes it will make a mistake, where it will do such smart things and then make a mistake.

Right.

Just thinking, why are you doing that?

Stop.

So I think there's definitely a lot of room for improvement.

But yeah, 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 and what everyone said was a graveyard market enterprise search.

It was like a graveyard, like, you know, of all these companies that tried to solve the problem and it didn't.

One of it was just that I think search is a hard problem.

In an enterprise, like even getting access to all the data that you want to search, it was such a big problem.

In the pre-SaaS world, there was no way to sort of go into those data centers, figure out where the servers were, where the storage systems were, try to connect with information in them.

It was a big challenge.

So SaaS actually solved that issue.

So like search products, like most of them, most of the companies started in the pre-SaaS world, they failed, because you could just couldn't build a turnkey product.

But SaaS actually allowed you to actually build something, you know, which is my insight was that like, look, you know, the enterprise world has changed.

We have these SaaS systems now and SaaS systems don't have versions like everybody, all customers have the same version, you know, they're open, they're interoperable, you can actually hit them with APIs and get all the content.

I felt that the biggest problem was actually solved, which was that I could actually easily go and bring all the enterprise information and data in one place and build this unified search system on top.

So that was actually a big unlock.

And by the way, the origins of Glean is so at Rubrik, you know, we had this problem, like, you know, we grew fast, we had a lot of information across 300 different SaaS systems, and nobody could find anything in the company.

And people were complaining about it in our surveys.

And I, and I was, you know, I always run ID in my startups.

And so there's a complaint that, you know, it came to me like I had to solve it.

So I tried to buy a search product, and I realized there's nothing to buy.

I mean, that's that's really the origins of how Glean got started as a company.

And so that was like, you know, one big issue, like, you know, the search SaaS made it easy for to actually connect, you know, your enterprise data and knowledge to a search system.

So that actually made it possible for us to for the very first time build a turnkey product.

But there are a lot of other advances as well.

You know, one is, you know, like, look, you know, businesses have so much information and data, one interesting, you know, facts are one of our largest customers, they have more than 1 billion documents inside their company.

Now here this, you know, when Elar and I know when we were working on search at Google, you know, in 2004, the entire intent was actually 1 billion documents, you know, there's a massive explosion of content like inside businesses.

So you have to build scalable systems.

And you couldn't build like a system like that before in the pre cloud data.

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 a bridge.

It's pretty heroic in general for a doctor to give you feedback like, hey, this sucked, and you got to do better.

Like, you didn't recognize the way I said this meant medication or I'm a gastroenterologist.

And I would never, you know, sequence my problems in my assessment and plan section of my note this way, it doesn't serve me well, and makes me look like terrible as a doctor or whatever, we get that feedback, we love it, it's oxygen.

But then we also get the feedback that's like, hey, this is amazing.

And I'm not going to retire anymore.

And I've got like years, decades left in my career now, thanks to this technology.

But in this channel, love stories, all of that feedback, a positive feedback, we just get it like programmatically funneled.

So any one of our people inside of the company can always go into that channel.

And it's like purpose, you know, it's like fulfillment immediately, like you immediately understand why we're all working so hard.

And why it makes sense because like being on this very telephone pole like journey these last couple years is obviously like it's news for so many of us and we're all kind of building new muscles, but it's it's a lot of pressure.

But this is my favorite bit of feedback.

So this love story comes from a doctor at Tanner health, which is a rural health system.

And she wrote to us, she wrote, 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 a bridge 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.

And we get feedback like that like every day, you know, and so like, there's there's dopamine hits, you know, and hyper growth.

And like, those are awesome.

But I think that they get us through like sprints.

But I think it's the oxytocin hits like this.

It's the purpose.

It's the fulfillment.

It's like, that's, I think, what I think we're really after in this company.

And so like everybody's mission driven out out there.

But I think this mission, like it hits me at least a little bit different.

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 with the guest.

Thanks for listening.