Breaking down the 2026 Stanford AI Index Report

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Welcome to another episode of the Practical AI podcast. Today it's just Chris and I, my my co-host and I, in what we call a fully connected episode. Where we try to keep you updated with some of the things that are happening in the AI news, and maybe, uh, share some practical, practical information that'll help you level up your AI and machine learning game. I'm Daniel Whitenack, I'm CEO at Prediction Guard, and I'm joined as always by my co-host Chris Benson, who is a principal AI and autonomy research engineer.

How you doing, Chris? I'm doing good, I'm excited. This is we we're doing the episode we're doing today. We've done a number of times over the years, the Yeah,

Stanford AI Index report, we get to go through it, it's always fun. Um, and kind of kinda level set, kinda how things are changing, and gosh, I mean, things are changing things are so fast right now. changing, yeah, Whew, so

and uh, for context. So some of you may or may not have have listened to our previous episodes where Stanford, Stanford's human centered artificial intelligence, uh, uh, center, uh, i i i institute, I forget, um, the exact of what they call themselves, but the human centered artificial intelligence effort there at Stanford. They publish this AI index report, and they've been doing it for a number of years, we've talked about it before. If you're interested, we're we're not gonna go into, like how it was created.

It's very rigorous, it's very data driven. You can go back and listen to episode 276. We had some representatives on from Stanford that actually shared, you know what it is, how it's created, and I'm sure that's updated somewhat over time, but that would be a great context for today. But there's a lot of takeaways here, Chris, and I think, you know, maybe we'll get through all of them. We can try rapid, rapid fire here to talk through some of these and share them with the audience and see, see, maybe our reaction to to some of these.

Some of them were a surprise to me, to be honest, Chris.

yeah, there there always are, cause, I mean, we, you kind of, y it, it kind of brings you back after, you know, with with the rigorous approach they have. We all have these perceptions, we're all watching the, you know, the news and all the AI hot things that are out there. And uh, and there's times where kinda it kinda level sets you a little bit, and then other times it kind of goes, uh, that. And I mean, you know, just kicking us off on number one, uh, on their top takeaways list right off the bat.

We kind of this is one of those places where we were going one way and then it didn't take the report. We were kinda. We kind of realized that things were changing back, but for a while, we were pretty convinced open source uh models were gonna, um, completely catch up with plateau models, cause that's the trend that we were seeing for such a long time. We realized a little while back that that wasn't happening for a variety of reasons, which we've actually talked about on previous episodes.

But the the very first thing they mention is AI capability is not plateauing, it is accelerating and reaching more people than ever. Um, and yeah, I think I I think we're seeing that in 2026.

yeah, so one one way. One of the ways they express this is that over 90 of notable frontier models were produced in in 2025. And several of those now meet or exceed human baselines on a number of things, and they and they go into those things. Obviously, one of the things, one of the hot takes that I'm always sharing, Chris, are these baseline or or these benchmarks? Let's say on PhD. level science questions. Uh, they're very, they're interesting, and I think they're some somehow representative of how we're advancing, but benchmarks in general are quite flawed.

Uh, so even with that, you know, caveat in there, it does seem like there is advancement that's that's happening. And you know, uh, a lot of that reaching or exceeding human level performance, it is is impressive and maybe scary for some people. I'm, I'm not sure, but

Yeah, I mean, we're. We've gotten these frontier models, you know, in recent months that are are just so capable, especially when combined with these new agentic systems. That everyone's, uh, been, obviously, a huge topic this year, um, and and able to productively do a lot of stuff. Which is creating a lot of, uh, upheaval in the job markets and how different companies are perceiving that. Um, but yeah, I mean, it's it's a new. It's very different from a year ago today, I would say, if you, if you well, look back

yeah, I I think, like one. They talk about, uh, the majority, four out of five university students using gen AI. And I, I was actually thinking, Chris as like a gauge on this. I was thinking back to my own PhD, uh, which it's about five years, um, long. And like, how long would that amount of work taken me with the tools that are available now? Both research wise, there was a coding element to it, there was a writing element to it, obviously, and I think like the amount of work I did was a lot.

But I think with the tools now, it's got to at least cut that down by half. I would, I would assume, and I don't know. Like, obviously, universities are wrestling with this and how to deal with it. And and you know, maybe people are just getting more done in their PhDs now, which would be, which would probably be good. But

a, and and I know we're already blowing through. We ha, we only had, we have f. We had a bunch of items to get through, probably not gonna get through them all, but I will note that. I I think that also translates into the workplace. I know, uh, in my own job, I am much more productive with the tooling that is cap, you know, that we are all using here. Um, and so the notion of what, what?

Not too far back, it would have been like a research project and you would've been trying to think about all the things you gotta do for that. And now that's like, it's like, I'm gonna dive into it and by the end of the week I'm gonna have this thing done. Um, that would've been a large body of work prior to that, and so, um, yeah, uh, it's definitely changing jobs, which is another I thing that I think we're gonna get to down the road.

Um, number two is, uh, AI model performance. Between the United States and China has closed, it's effectively closed, um, and we're looking at two. It. No, no longer kind of a leader follower uh effect, but two co leaders in the world market. Where

the a, and actually, this was one of the ones. Well, I don't know, maybe there there's different elements of this, Chris, I would say, just in my own practical experience on the uh, uh, you know, people use and govern, you know, both closed and open models in in our platform now, so I get exposed to kind of both of those, I would say. On the open model side, just practically, China seems to clearly have the lead now.

Maybe they do that's that's different on the, I guess, different on the closed model side, if so, maybe there is a little bit of nuance there where, uh, fair maybe. In my mind, I had this perception of of a of Chinese models being almost superior. But that doesn't really factor in the frontier model, closed model provider side of that.

that, that, I mean, that's true, we and we talked about that recently on on an episode just a a few weeks ago. Um, and the, uh, you know, the notion of, uh, in a lot of ways, the US. has kind of walked away from open models to some degree. Um, and you know, meta, you know, with meta walking away, they're, they're now going entirely closed, and that was kind of leading the US. contingent. Um, it's not to say that there aren't smaller, but in that top tier, we've kind of walked away.

Whereas China has largely embraced, uh, the open model approach, which, uh, I think the fallout from that in the West will be interesting. In terms of how much, you know, there's a certain amount of geopolitical uh division across, you know, between the West, uh, and the East. In terms of of how they, uh, adopt models and what models are okay to use in different contexts. And so, um, as we see open models, uh, predominantly, uh, I, in the large scale happening in the East, closed models only in the West.

Um, how that ends up shuffling things will be, uh, an interesting thing to watch in the months and years ahead.

Yeah, and connecting a little bit more of that dynamic kind of us and abroad dynamic, uh. Takeaway number three on the AI index report was that the U- United States hosts the most AI data centers. But the majority of their chips are fab fabricated by a single Taiwanese foundry. So, uh, it th- this one, actually, Chris, so not the chip fabrication piece. I, I knew that piece. But you know, you always hear about China just spinning up data centers everywhere.

So it was interesting to me to hear that the the US. still hosts the most AI data centers. Cause, obviously, you know, if you're putting in a data center in some town in the United States, you could get, uh, the local city council against you. And people are up in arms and there's more hoops to jump through. Whereas in China, you know, a lot of, a lot of that can just happen, um, I I agree and and there's a huge amount of investment.

So, yeah, th- this one was actually pretty interesting to me to hear that current state.

I, I'm. It'll be interesting, like when this same report comes out next year, you know, year by year, to track that, um, and see if our, if the if the United States, kind of that ten time number, uh, that's in here shrinks, maybe even shrinks very rapidly. Uh, and and so we'll see, we'll see what happens on there.

Yeah, yeah, for sure. So takeaway number four. We're we're gradually working our way through here. Chris. Um, AI models can win a gold medal at the International Mathematical Olympiad, but cannot reliably tell time. An example of what researchers call the quote jagged frontier of AI. Interesting it. It almost seems like I remember we had a guest, I'm pretty sure, I, I don't know. I remember talking to them. I think they were on the show. Sometimes I forget what was on the show and what conversations I had in We real life.

Chris, I don't know we go talk if we do that

to a lot of people same. Yeah, so, we do.

but at some point there was a conversation that happened with, um, uh, someone from the Allen Institute, uh, for AI. And a lot of what they were looking at for some time was around, like, common sense. So AI models can do a lot of really impressive things, but uh, when it comes to common sense, it's like they, uh, fall, fall over. Because there's no actual connection to the real world, right? They're producing tokens, or they're, you know, producing pro, you know, uh, to tokens based on probabilities of what they've seen before.

And so there's these many seemingly seeming coherence and impressive things that happen. And then all of a sudden they can't do the most simple thing that involves some connection to the real world. Like here, you know, telling time, for example.

Yeah, eh? The example they talk about is Gemini Deep Think getting the gold medal at the IMO. But only being able to read an analog clock, uh, fifty, one of the time in terms of accurately, um, and and and they offer some other stats along the way. And I think, you know, this goes back to another topic that we've talked about a number of times, and that is we're still talking about language, you know, being what these models are are trained on, and we've we've talked, you know.

It's becoming increasingly popular to talk about the notion of what a genuine, what you know, it's. There's several names it goes by, but a world model, you know, something that actually has context for all the things in life. Because y, you know, you don't have that with, uh, with our existing frontier models that are, you know, based on on training on language. So, uh, there's a lot of research. I know, uh, you know, that's famously. Yann LeCun, one of the the godfathers of AI, has talked about that many times over the last few years.

Um, the need to move past LLMs and have world models that actually have context, so, um, and increasingly, especially, I know, as I as my world over the last few years has gotten more and more focused on on edge cases and autonomy. Um, the notion of world models and how they would impact, uh, our field has become increasingly important. So it'll, it'll be interesting as well to see how, um, how this measures up, you know, next year in the same report, uh, as we get to that point on on I think, those kind of upgrades.

maybe a bit of an opinion here, Chris is I. I almost think we're not being fair in some sense to the. I don't know if it's to the models or to the the way people do AI. Because, y, you know, like Claude, for example, like an anthropic model, knows nothing about the tickets in my ClickUp platform, right? And so you could call that model dumb. Oh, it doesn't know, like, blah, blah, blah. But I can perfectly well just connect Claude via Claude Code skill to my clickUp.

And all of a sudden now I have all that context about what I should be working on this sprint. And all Fair enough of that context is there, and it knows about my PRs and all of this stuff, right? So it's kind of like what we talked about with, Um, when we were talking about Hermes agent, where the. No one would expect a a brain absent a body to be sort of take useful action in the world.

And so that agent harness around which we surround the model is really part of that connection. So it is it could be that, like, there are these world models and such that are relevant, and that seems like good research. But also, like, part of this is that these models need a body, they need a harness around them, right?

we've, and we've. We've addressed that a whole bunch of times on the show and and the the you you can't a model, uh, a model in isolation doesn't do you a whole lot of good. Um, you've gotta have that connection with the world. And uh, and I think as things evolve, we will. I. I personally, my own, uh, personal belief is that world model development requires the same. Uh, you have to have feedback from the real world to be able to, uh, incorporate that into training to actually get you what you're looking for.

And as inspiration for that, if we look at our own human brains, we're born, we're babies, we don't, you know, we have these amazing baby brains, but they they haven't gotten a lot of experience against the real world. And it is all those feedback loops in those first two decades of life that kinda get us to, uh, to functioning. So, uh, there'll be some sort of analog, presumably, uh, to that notion, uh, in the world model development world as we go forward.

So

Speaking of AI in the physical world, takeaway number five robots still fail at most household tasks, even as they excel in controlled environments. Do you have any any robots in your house, Chris?

I, well, uh, only the ones that most people have, uh, we have the vacuum going around and such. But, um, you know, uh, I keep seeing, you know, especially, uh, this is a much bigger thing in China than it is here, um, with with robots, uh, in production in a lot of households. Uh, I saw something just a few days ago about a humanoid robot that, uh, you know, is doing elderly care and washes the dishes and things like that.

And that was exactly what I was wondering, is like, um, if you took that out of of their version of CES, um, and and kind of explore it, I I wonder. When you have an, uh, kind of a a somewhat unique environment, you know, as different configurations of households and what duties, how well that really performs. And I honestly, I, I don't know, I haven't had direct exposure to robots of that that nature. Yeah, yeah, I and But I I guess will one, but I will, you're I'm ready, I'm ready, yeah, ready.

and I guess controlled environments here, they're referring to maybe manufacturing facilities, or, um, they also mention kind of software based simulations, right? And uh, and yeah, that that that will be an interesting one to follow for for sure.

I'm. I'm just curious that when we. When I get my first humanoid robot and we assign it the tasks around the house and I have five dogs running around the house and the dog starts jumping up on it to play. Uh, and it may be the puppy at first, but then the the big dog that weighs, you know, eighty pounds jumps up on it. Um, it will be interesting to see, uh, if it can survive the chaos of the Benson household.

listen, one, one of the I forget the so. We had a a friend in the restaurant industry and he gave my wife and I tickets to. I forget the name of the show. It's like the Chicago Restaurant Convention or something. It's basically you could go there. There's a bunch of vendors that sell products into restaurants. So everything from like utensils to appliances To to software to whatever, you know, whatever, and uh, so you know, big networks of restaurants so go there and look at things.

And they had all the robots there in, you know, different sections that did different things. And I have to say, you know, not having exposure to that world a ton, but kind of expecting way more than I saw. I, I guess I. WA- I was fairly disappointed. Like most of the robots that did, like cooking, for example, it seemed to just be, you know, like, uh, you, the the washer dryer that spins, and you know, it spins.

Aro- It was basically like a drum like that that heated up and you it just sort of dropped ingredients in the drum and it spun around and kinda stir fried them or something like that. It's like, Uh, I, I was. I was disappointed that I didn't see any like humanoid robots, like chopping up, you know, making sushi or or something. Uh, very far from, uh, yeah, the the heated washing machine was very far from what I expected to

what you're envisioning? I I think I. And and while we're obviously questioning, uh, you know, how, how real the performance capabilities are, I do, uh, at least. My personal belief is that I think you're gonna find, um, a a l- a. A certain leveling up in China above what we've done in the US. and probably, uh, a fairly substantial one at that. So, um, I. Is that because we'll see,

of safety restrictions in the US. or is,

I'm, I'm not actually sure. Uh, I I think I think that, uh, my sense is that, uh. Robots have just been a higher priority for quite a long time. Uh, drones and robots, obviously. And you know, you know, if we go back to the turn of the millennium, like looking way back, and we're looking at old school drones going up and celeb, you know, doing performances way back before. Uh, we were really even thinking about such things at all.

Here. I think that if you've been doing something for a long time and it's more of an evolutionary step each way. Um, whereas for us, we are, we are surging, uh, here in the US. but I think we're coming from behind on the experience side of that. So, uh, it'll be interesting to see how that plays out over time.

yeah, Ma- makes sense. Well, something that is playing out over time. NUm Takeaway number six Responsible AI is not keeping pace with AI Capa- capability, with safety benchmarks lagging and incidents rising sharply. Chris Uh, obviously, this one, uh, hits close to home for me. This is part of what we're hopefully helping people deal with. But it also reminded me of, uh, you know, of course, incidents and other things that we've seen from my work with Prediction Guard.

But also a a show that we had actually somewhat recently. So back in February episode 346, which was AI incidents, audits and the limits of benchmarks, which basically hits directly at this. We had Sean McGregor on that show and and he was talking about the AI incident database that he was helping helping create and and manage. And yeah, that it was very interesting, just the the diversity of AI incidents that we're seeing now and the sharp rise in those.

And this is also, you know, only documented AI incident, you know, cases. There's many things that are happening just anecdotally that I see that I'm sure aren't being documented in that AI incident database, right?

Yeah, you know, I'm gonna, I'm gonna make a comment that probably will surprise most people, uh, tuning in, and that is, um, uh, 1st of all, making it very clear. I only speak for myself and not for my employer or any other organization. Um, I actually think people would be surprised in the defense industry that there's probably more guardrails and responsible AI efforts around our industry than most commercial industries. Um, there are federal regulations here in the US.

that prohibit, uh, certain things that in the commercial space, people might just surge and go do, um, in terms of safety issues. And so, uh, as I was reading that earlier before the show, I was thinking, you know, I'm actually in an industry that it may hold us back at times because, uh, we're not surging the way the commercial industries have the freedom to. But I think it also, um, there's, uh, there's quite an intense focus on, um, keeping things that need to be safe, safe.

Um, and I have noticed that. And sometimes, as someone who is always enthusiastic on new technologies, I get, uh, a little bit frustrated. But then I stop and go, No, I'm glad, I'm glad we're that way. So, um, I just thought I'd mention that, um, I I that

I I think it's encouraging and a good inspiration for the rest of us to consider. Those those responsible AI governance, enforcement, you know, policy, whatever, however, what, what, whatever form that takes in your level of maturity as an organization. I think there are, there are those out there that are pushing, uh, that direction. And we've seen development even over this last year where people, I think, are moving beyond the sort of trust me phase of of AI governance towards, uh, you know, exportable proof and, uh, s, even certification types of things.

And I think we'll see, you know, one of my predictions this coming year, I think, is we're gonna start to see some of that, uh, needed exportable proof being part of even audits and certifications, whether that's SOC two or AI specific types of certifications for companies.

I. I not only agree with that, but I think that the marketplace will demand it. Uh, as we go forward, I think it, I think we will, uh, continue to have some big news events where things are going off the wheels, uh, from various, uh, organizations and industries at different places, probably a variety of them. And there's gonna be a point where where people say we need. We need to know that there are safety measures in place, uh, before we're willing to deploy it, uh, within our organization.

And so I I think the market will will demand that going forward.

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Um, well, Chris, we are almost half ish way through, uh, the the the takeaways we w- might not get to all of them in detail. But number seven, the United States leads in AI investment, but its ability to attract global talent is declining. Interesting,

Yes, it is interesting. It's, uh, I think we are, and I, I'm not. I'm not terribly surprised by that one either. Um, as we have seen diversification, um, and also, frankly, uh, as the as as political priorities in the United States have changed. Um, I'll leave that to to to folks tuning in to, uh, to decide kind of how they're looking at it. But I think that that has also impacted that. Um, if you're, it's certainly hard to attract Gl.

If you're talking about global talent, it's hard to attract global talent. If, uh, things that are ancillary to that, th- things like immigration are are a challenge. So I don't, uh, for me, at least, given the the current circumstances, that wasn't a surprise to run across that. Um, I'm, I'm hoping that, uh, that we don't lose our edge in that capacity.

yeah, the the number that shocked me in this report was, I guess, the scale of that. So apparently there's been an eighty decline just in the last year in terms of the number of AI researchers and developers moving to the US. Um, which, yeah, yeah, uh, To the point is is pretty,

I just made right there.

is is pretty, uh, astounding. Um, yeah,

yeah, so it's, uh, y- you reap what you sow. So we'll see how things change in the in the years ahead. Um, but uh, yeah, interesting, interesting point. I'm I'm curious, uh, with, uh, a any thoughts on kind of future of AI investment? You know, we're st- we're leading right now. Do you have any, any positions on your side on where you think things will go?

yeah, I mean, I I think the uh, the US. still kind of corners the market on uh. VC driven startup f startups that are funded by that world. And, you know, Silicon Valley still h- holds, uh, a special place there in terms of the V VC funds that are there. You know, other places too, New York and kind of growing markets in the Midwest and and that sort of thing. So I think that's gonna be, that's gonna hold true, uh.

The report also shows still, the greatest number of companies that are being started are starting in the AI companies are starting in the US. and maybe funded by US. VCs. But now, in this world, I think a couple things are true. It. It's becoming more and more possible to maintain smaller teams and get a lot done. And so there's less people needed to build a company that's even doing tens or hundreds of millions of dollars in in revenue.

But then also, teams are necessarily distributed these days. And maybe it's not necessary for those AI researchers or developers to move from other places around the world to those countries to do that work. So Good point, even if the companies are funded there and the VC is here, that that might not reflect where the individuals in the company live or operate. Uh, yeah,

That makes perfect sense. Um, and, uh, you know, talking about, you know, VC and the the adoption rate and stuff, um, yeah, we, we, the, uh, the last few months, the amount of AI adoption, um, has just skyrocketed. People were u- until until 2026, maybe tw- you know, late 2nd half of 2025. I, I knew a lot of people who were not in technology and they were touching on different model programs here and there.

Uh, most of the, you know, the the people that I would be talking to would be touching the closed frontier models, whichever one they particularly went for. But we've seen a marked improv- uh, improvement in terms of adoption. Um, I really don't think I've run across anyone recently, um, including, I'm all, all age brackets. Um, I have friends who are in their nineties, who are using, uh, who are using the tools that are out there now.

Mostly free, uh, mostly free. And that's what Stanford had noted, uh, is the different free tiers, but um, but that has, uh, that's been interesting to see. But I noticed that, uh, I think the one thing that may have surprised me was that Stanford had noticed that. We are still, um, in the US. ranked twenty fourth at only a twenty eight, three adoption. So obviously I'm not, uh, I'm not the, uh, you know, representative in terms of my own experience of that.

What are your thoughts there?

yeah, it's it's super interesting. I I don't know, uh, the full economics of some of these free access, uh, systems and how much you know usage. For example, like Gemini's Google, Gemini's getting off of usage on Android phones from free users versus like paid workspace accounts. I, I, I don't know how all the economics play out there, but certainly, I know, just anecdotally, the last couple flights I've been on, for example, I look around. And of course, people are on their phones.

And a significant number of those people on their phones are chatting with an AI, uh, app of some type, and some chatting with that AI app throughout the entire flight. On, you know, Wi Fi, and, you know, not even watching a movie or whatever like it it. So,

I've been guilty of that myself.

yeah, yeah, and so, um, yeah, it's it. It will be. It will be interesting to see how that that level of usage continues to spread. Um, uh, I don't under, like I say, understand some of those direct to consumer mechanisms as well as, um, some of the B2B type of things that that I that I interact with day to day.

I will. Well before we leave the topic of kind of, you know, general population AI adoption. Um, there's something that I've noticed that I was meaning to bring into the show at the right moment anyway. And that was, um, my mother is a is a technologist. She's retired mid eighties. Um, uh, did old school AI way back, uh, but as she's been kind of re engaging, uh, in recent times, and, uh, I noticed that she was, um, she was talking to me the other night about.

She likes to paint. And then she'll capture thing, uh, an image in Photoshop historically, and work on it in Photofy and get it just the way she wants it. And I was like, Well, mom, you could do that in any of these tools and just have it do it, like, just tell it what you want and it will do it. And I realized that she was taking pride in working in Photoshop with her skills there. And I got about halfway through trying to convince her, and I backed away, cause I suddenly realized, like, this is her hobby and there's fun.

And even if she could, in a productive way, move right to the end goal. Maybe this is a moment where AI is not the right thing to bring into play just because she enjoys doing it. Um, and so I just thought well, I wanted to bring that human element back into it, that not AI for all things is always the answer.

yeah, I I think that there is a distinction there, Chris, because you could look at other other examples of this, right? It's it, I, I know people that brew their own beer, right? There's no reason conceivable that people should brew their own beer. Because they can just go down the street, uh, for less money, with probably better results, and and and get something, you know, that that tastes great. It's already cold, right? But that's not, that's not the point, right?

To to what you were saying. That's not why they're doing things in that way. And the it'll be interesting to see what kind of, uh, uh, w, what elements also. See, we we see a resurgence of, I don't know, people that just want to use use Excel and do analysis because they enjoy doing it. Uh, I I think what is true is if you're in a job where there's productivity expectations and you're doing those things, then that's no longer gonna be acceptable, right?

So, in you're speaking the same way,

hobbyist and professional.

yeah, in the same way that, like some people might like writing physical snail mail letters. But if you were to insist that you're only going to write physical mail letters, send them through the mail as part of your company communication, and you're not gonna use email, that's not gonna work. In the world that we live in, right?

Yeah, I, I will tie a bow on this by saying, to your example, once upon a time I did. I took a a hand at at trying to brew some beer, uh, and and also wine, and both my beer and my wine were terrible. But I took great pride and I drank, I drank them, because I, I took a lot of pride in it. Whereas I certainly wouldn't have done that in a professional context. But yes, I there.

W- y- there. The. It's interesting to see the human element inching back in, uh, and well, recognizing that there's a difference.

so you were gonna try to bow on this. But I I think one more anecdote Okay, fair is really intere- enough w- is really interesting. Because one of my friends that, uh, brews their own beer. One of the things that they told me that they did recently was they said they. They actually used their creativity in a I in an AI tool and said, I'm, I kind of wanna brew a beer. That's, you know, like this, and it has this ABV and like these notes, and, uh, it's kind of this style, I kinda want it to turn out like this.

And he gave all that description and actually had the AI system create the full bill of materials and and recipe for that. And then he went to the brewing store and, you know, actually had the person there like, Hey, could you look at this and see if this is legit? And they're like, Yeah, this seems, this seems great to to us, and they got that, and he, he brewed the beer, and it was, it was g- It worked out great.

So I think actually this could be a spark to some of those hobbies on

there you go.

the o- on that side of things. I know, you know, even Well, I just just, of course, on like travel and trips and that sort of thing. I heavily use AI systems to help me plan out things and do research and such. Uh, and that's, you know, part of what I enjoy about doing the planning of trips or something like that. Yep, well, moving on, uh, yeah, what are we on? We're on takeaway nine,

that's right.

which is productivity gains from AI are appearing in many of the same fields where entry level employment is starting to decline. That's, uh,

hm. I think we've talked a lot about this on the show over the months. Yeah,

There's no way you can get a junior software dev position writing SQL queries anymore. that's, that's right, that's Yeah,

gone. Um, it's, it's interest and and and the senior level people, ha, have have been learning very rapidly that it is time to embrace. Um, I, I really, it's funny as we went into new year's. Uh, I knew a lot of people who were still pushing back on that, uh, at this point, uh, as we're recording in late May. Um, I can't think of anybody I know that's pushing back on that at this point, at least not not that I know well enough to have these conversations with.

So that's changed, the world has changed in a in a very short amount of time, certainly in coding, but I think in a lot of other areas. You know, you talked about Excel if you're, uh, not a technical person, but you're using Microsoft Office. And to your point a few minutes ago, um, a a lot of the, a lot of the basic stuff, uh, you're gonna be using an AI assistant in your tool. And I think that's going, uh, that's going across many, many industries.

So not not surprising.

yeah, yeah, yeah. And I I think I I've been thinking about this over the previous weeks, Chris. Also because, I mean, uh, we have hired folks into into our company and I'm sure I'll be part of hiring process e processes in the future. And it's interesting because th- there you could say there's no longer the chance for the int- entry level jobs, but there's still a chance to hire people in and give them e- even if they're a more junior engineer.

Like we have, for example, in our company, maybe, uh, like we have our own repo with all the skills, code, code skills that are relevant to our stack. And connect to this and that and help you get up and going and a and really like that, that's, that's a lot of power that you can give someone now. Obviously, there are debugging things and architecture things that are very ta- take a very skilled person, a more senior person to get to the bottom of.

But I think, um, I guess what I'm saying is, I think these tools can also help junior folks coming into a position to level up more rapidly than they were doing before. And maybe even if universities or educational systems embrace those tools and help them level up even before they're on the job market, then they might, you know, have have more of a a more of a chance. Not to, not to, not, you know, I. I definitely acknowledge that jobs will be lost here, there, there certainly will be right, and There will that that is be, a hard thing for many people.

yeah, uh, going back before we leave this cause, I think this is important, there there are other things we can skip over, uh, in the interest of time. But I think the way w- I think you're a really big point. There is the way we're learning things has to change too, in that, and I'll share a a two second uh uh experience. Is that? Um, both of us have been through many programming languages over the years.

And um, and I had, uh, gotten an interest in Rust. And I was dipping in and out of it and not really taking it on board, and I'd get caught up into something else. And um, and it, ha, it's famous for its steep learning curve. Um, it's taking a little better this time. Because, uh, one of the things that I've done to try to get go from very, uh, entry level to beyond that is to to use, uh, the tools that are out there, using Claude Code, uh, and and and creating it.

But also having it explain and having discussions about what's going on and why. So it's not just make the thing, but it's M. Let's let's have a conversation about what the thing, how you make the thing and why the choices are being made. And you can use the model to say, Well, why are you doing that? What's the, what's the rationale? Would it make sense to do this and have that? And and I? That has been, um, transforming for me, uh, who is not, uh, fresh out of college, um, to to continue to learn at a rapid pace, um, and it's been a great experience, it's been different.

But I would encourage people to be open to that. And so don't just have the model do whatever your thing is, don't just have it do it, have it explain and share in the load as you go. So that it is a it is a creative, but also a learning process as you do it, and then you'll come out of it better than you started as well. Um, so I, I, I really wanted to to just kind of get out, get out there and urge people to give it a shot,

Yeah, it does tie into one of the other takeaways, uh, w- which, yeah, Chris, we can just mention a few of these as we get closer to closing out here. But formal education is lagging behind AI, but people are learning AI skills at every stage of life. That was one of the other takeaways. Uh, I think, you know, related to, related to what you're, what you're talking about. And yeah, it's. It's like, I I think the stat they gave is eighty of high school and college students now s use AI for school related things.

But a very small percentage of teachers, uh, you know, have any sort of policy in place around around that usage. Or e- either positive or negative, right? Which I think both of us would hope that some of that is on the positive side. And there's teachers and universities that are helping students learn how to use these tools and encouraging their use versus trying to always police it and shut it down, which isn't gonna work,

which is the right. Yeah, it's not gonna work. And I have an incoming high school student of my own and I have. I have been telling her for years now to use the tool, I want her to use the tools, but I'm. But going back to my previous comment, we, I'll sit down with her and we will use the tools to learn so that she actually comes out. And, uh, by way of example, even though she's a heavy user of AI tools for school, um, without those tools, she'll go in.

She went in for her finals, uh, in middle school, uh, just now, straight as you know. And that's without any AI tools being available to her. To do that, she learned the material and that's trying to use it the right way, use it to where you're learning. Um, in addition to just getting the job done, like a lot of students might do so, it's it's the right way to do it. And I, I as a parent, I would, ed, I would urge teachers, uh, and I know you're restricted by your school policies, um, and such, but um, try to be open to that, uh, open to to to taking on the new tools, uh, with your students.

So

Yeah, well, just to mention a few of these that we didn't get to, Chris. I encourage people to look up the report, we'll link it in our show notes. You can read all the details. The full report is 425 pages long, so you probably wanna stick that into some type of AI thing and and ask some questions and and work through it. Some of the other ones that just so people know them are AI's environmental footprint is expanding.

Uh, AI models for science outperform human scientists, though bigger models do not always perform better. AI is transforming clinical care, but rigorous evidence remains limited. AI sovereignty is becoming a defining feature of national policy, and AI experts and the public have very different perspectives on the technology's future. So all of those super interesting, of course. Go and look at the article, dig into the details, have some fun exploring it. And and thanks again to Stanford for continuing to to do always this great work.

Yeah,

they do, great, great reports every year. We love I. We wait for this every year to dig into and have fun with it.

so awesome, Chris. Well, have have fun yourself with, uh, with your non non humanoid robots at home and the AI tools for for school and all the things.

Absolutely, see you next time.

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