The Exchanges

Q now. Um, what's going to happen to all the programming jobs? Like, it used to be the case that if you were a CS major, there is a very clear path to, like, a very stable, like, upper middle class background where you get, like, a good stable job as a, as a programmer. Um, but, like, Are those jobs still going to be here in 10 years? Like, yeah.

A Yeah, like my, my parents were really proud when I, uh, you know, graduating, I, you know, got my degree, and then I got my job at Microsoft, and I was a level 59 PM, uh, you know, lowest of the low, but I had health insurance, and my parents were really, really proud of me. And, you know, one of the fears, frankly, like, that we're hearing, uh, and it's sort of, you know, Coming out in the numbers is that will there actually be jobs? I think it's a tricky thing right now with the advent of intelligence, you know, some of the simplest things that people rely on entry level people right out of college for, uh, they're not hiring as many of them anymore. And, you know, the craziest stat, I think this came out of, uh, uh, the New York fed in February of this year, um, computer science majors, uh, You know, obviously this is not the people in this room. This is just, like, out of, like, you know, a normal distribution of all computer science majors. 6.1% in unemployment in February of this year. Art history, in contrast, was only three point oh percent.

AI assessment note: “6.1% in unemployment in February of this year. Art history, in contrast, was only three point oh percent.”

Q I think Gary's got, like, a great point on this. Um, it's basically, like, become, like, a forward deployed engineer, right?

A Yeah, just, I mean, go undercover, I guess. Like, go, go and figure out what people actually need. And, um, yeah, there are just too many examples of billion dollar startups that we got to see. I mean, I always think about Flexport. You know, here's this guy who literally became one of the top importers of medical hot tubs. Like, I don't think anyone wakes up You know, and graduates and decides like, hey, I really need to become one of the foremost, you know, import exporters of, ah, of medical hot tubs. But, you know, he did it. He, they, they did, they also, um, I think were one of the first, the biggest e-bike importer. But then, you know, basically being in weird parts in the economy, um, caused them to understand just things that, that, ah, the, the other person, you know, the sort of 1010 thousand other people who want to start startups, like, they didn't have that knowledge. And so, Sort of your ability, your, you know, if you're here, like your inherent ability already is like one part of the Venn diagram, and then the other part is just something weird. It's literally just like, where does your interest come from? Like I'm really taken by to what degree both OpenAI and SpaceX, for instance, where, uh, you know, the Genesis came from like interest and a hunch and just Like, not really any commercial intent, and yet, you know, coming out the other side, uh, that was enou…

AI assessment note: “Yeah, just, I mean, go undercover, I guess. Like, go, go and figure out”

Q What are you seeing about, like, so the concern around data privacy is another big reason. Like, are you seeing that as being enough, like, are people worried about giving these data sets to OpenAI?

A It's really interesting. I mean, whenever you have something so new like this, it's actually, um, sort of resets the clock on the competitive landscape again. So, you know, you almost can expect all the same things will happen again. Um, you know, just as 10, 15 years ago, cloud was brand new, and then you had cloud cybersecurity and cloud strike and all these companies sort of come out. Um, you know, we're seeing the first wave of cybersecurity companies, you know, like prompt armor. So they sort of wrap your API calls And, uh, what they actually have figured out is that for a lot of large language models, if you do any sort of fine tuning or training with private data, you can actually just speak to the model and get it to spit out your private data again, and they have a solution that stops it. So it's so interesting because, you know, it's entirely possible, you know, they're basically creating a new industry again, um, of cybersecurity for LLMs, sort of in the same way that Cloud opened up that space and created cybersecurity for the cloud.

AI assessment note: “we're seeing the first wave of cybersecurity companies, you know, like prompt armor.”

Q I think you've put this in a really interesting way before, Carrie, where you sort of saying that every founder has become a forward-deployed engineer. That's like a term that traces back to Palantir, and since you were early at Palantir, maybe tell us a little bit about how did forward-deployed engineer become a thing at Palantir, and what can founders learn from it now?

A I mean, I think the whole thesis of Palantir at some level was that, um, if you look at Meta, back then it was called Facebook, or Google, or any of the top software startups, That everyone sort of knew back then. And one of the key recognitions that Peter Thiel and Alex Karp and Stefan Cohen and Joe Lonsdale, Nathan Gettings, like the original founders of Palantir had, was that, uh, go into anywhere in the Fortune 500, go into any government agency in the world, including the United States. And nobody who understands computer science and technology at the level that, you know, at The highest possible level would ever even be in that room. And so Palantir's sort of really, really big idea that they discovered very early was that, uh, the problems that those places face, they're actually multi-billion dollars, sometimes trillion dollar problems. And yet, uh, this was well before AI became a thing, you know, I mean, people were sort of talking about machine learning, but, you know, back then they called it data mining. You know, the world is awash in data. These You know, giant databases of people and things and transactions, and we have no idea what to do with it. That's what Palantir was, is, and still is, that, um, you can go and find the world's best technologists who know how to write software to actually make sense of the world. You know, you have these petabytes of data, a…

AI assessment note: “go into anywhere in the Fortune 500... nobody who understands computer science”

Q How do you develop taste? Yeah. When you don't come from a classically trained world, which would be interesting for next generation.

A Well, you have to, because if you don't, the startup dies, right? So let's say this founder, they go off, they have 95% written by AI. The proof is in a year out, two years out, um, they, you know, have a hundred million users on that thing. You know, does it fall over or not? And then one of the things that's pretty clear is these systems, uh, you know, in the first The first versions of reasoning models , they're not that good at debugging. So you actually would need to descend down into the depths of what's actually happening. And if you can't, then you got, I mean, let's hope that they can go find another architect. They're gonna have to hire someone who can.

AI assessment note: “Well, you have to, because if you don't, the startup dies, right?”

Q is the, this is the thing that people haven't really, we haven't gotten to as a field as much, but looking at how, how does the brain process information? Can you add new brain areas? Are there ways to understand how the brain is like, what, What is going on either to use this to build smarter machines or to think about how to treat things like depression or addiction.

A I'm taken by, ah, to what degree right now it's about sort of, um, taking someone who has a condition or a disease and then bringing them, like, sort of restoring them to, like, sort of capability, right? So I think that's playing out in AI right now as well, right? Like you had computers that had no ability to do, like, any sort of Pure cognition, or like, you know, and, uh, you know, no neurons, and then suddenly a bunch of neurons, and then AGI is sort of like what a human can do. It's sort of like a restoration of capability. And then, of course, there's like this other thing after that, which is, you know, uh, ASI, super intelligence. Do you ever think about what that might be down the road? You know, what is that for BCI?

AI assessment note: “Do you ever think about what that might be down the road?”