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 share some practical information that'll help you level up your AI and machine learning game. I'm Daniel Wightnack, 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're doing the episode we're doing today, we've done a number of times over the years, the Stanford AI Index Report. We get to go through it, it's always fun. And kind of level set kind of how things are changing. And gosh, I mean, things are changing so fast right now. Things are changing.

Whew. Yeah. And for context, so some of you may or may not have listened to our previous episodes where Stanford's Human-Centered Artificial Intelligence Center, Institute, I forget the exact of what they call themselves, but the human-centered artificial intelligence effort there at Stanford, they published 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 not going to 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, 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, and there's times where kind of, it kind of level sets you a little bit and then other times it kind of goes that. And I mean, you know, just kicking us off on number 1 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 kind of, we kind of realized that things were changing back, but for a while we were pretty convinced open source models were gonna 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 very first thing they mention is AI capability is not plateauing. It is accelerating and reaching more people than ever. And Yeah, I think, 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 these benchmarks, let's say, on PhD-level science questions. They're very, they're interesting, and I think they're somehow representative of how we're advancing, but benchmarks in general are quite flawed. So even with that caveat in there, it does seem like there is advancement that's that's happening and, you know, a lot of that reaching or exceeding human-level performance is impressive and maybe scary for some people, I'm not sure, but.

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

Yeah, I think like they talk about the majority, 4 out of 5 university students using GenAI. And I was actually thinking, Chris, as like a gauge on this, I was thinking back to my own PhD, which it's about 5 years, long, 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 assume. And I don't know, like obviously universities are wrestling with this and how to deal with it. And, you know, maybe people are just getting more done in their PhDs now, which would be— which would probably be good.

But— and I know we're already blowing through— we only have— we have— we had a bunch of items to get through, probably not going to get through them all. But I will note that I think that also translates into the workplace. I know in my own job, I am much more productive with the tooling that is, you know, that we are all using here. And so the notion of what, what not too far back it would have been like a research project and you would have been trying to think about all the things you got to do for that. And now that's like, it's like I'm going to dive into it and by the end of the week I'm going to have this thing done. That would have been a large body of work prior to that. And so, yeah, I mean, it's definitely changing jobs, which is another thing that I think we're going to get to down the road. Number 2 is AI model performance between the United States and China has closed. It's effectively closed., and we're looking at two, it no longer kind of a leader follower, uh, effect, but two co-leaders in the world market.

Yeah.

Yeah.

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, 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 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 Chinese models being almost superior but that doesn't really factor in the frontier model, closed model provider side of that.

That, I mean, that's true. We, and we talked about that recently on, on an episode just a few weeks ago. And the, you know, the notion of, in a lot of ways the US has kind of walked away from open models to some degree. And, you know, Meta, you know, with Meta walking away, they're now going entirely closed and that was kind of leading the US contingent. It's not to say that there aren't smaller, but in that top tier, we've walked away, whereas China has largely embraced the open model approach, which I think the fallout from that in the West will be interesting in terms of how much— There's a certain amount of geopolitical division between the West and the East in terms of of how they adopt models and what models are okay to use in different contexts. And so as we see open models predominantly in the large scale happening in the East, closed models only in the West, how that ends up shuffling things will be 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, takeaway number 3 on the AI Index report was that the United States host the most AI data centers, but the majority of their chips are fab fabricated by a single Taiwanese foundry. So, uh, it, 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 US still hosts the most AI data centers because obviously, you know, if you're putting in a data center in some town in the United States, you could get 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 that can just happen. And there's a huge amount of investment. So yeah, this one was actually pretty interesting to me to hear that current state.

I'm really— it'll be interesting, like when this same report comes out next year, you know, year by year to track that and see if our— if the— if the United States kind of that 10-time number that's in here shrinks, maybe even shrinks very rapidly. And so we'll see. We'll see what happens on there.

Yeah, yeah, for sure. So takeaway number 4, we're, we're gradually working our way through here, Chris. 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 almost seems like I remember we had a guest I'm pretty sure, 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 real life, Chris. I don't know if you had the same.

We talk to a lot of people. Yeah, we do.

But at some point there was a conversation that happened with someone from the Allen Institute 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 when it comes to common sense, it's like they, fall over because there's no actual connection to the real world, right? They're producing tokens or they're producing 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. The example they talk about is Gemini DeepThink getting the gold medal at the IMO, but only being able to read an analog clock 50.1% of the time in terms of accurately. 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 trained on. And we've, we've talked, you know, it's becoming increasingly increasingly popular to talk about the notion of what a genuine— there's several names it goes by, but a world model, something that actually has context for all the things in life because you don't have that with our existing frontier models that are based on training on language. So there's a lot of research. I know that's famously Yann LeCun, one of the The Godfathers of AI has talked about that many times over the last few years, the need to move past LLMs and have world models that actually have context. So, 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, the notion of world models and how they would impact our field has become increasingly important. So it'll, it'll be interesting as well to see how, how this measures up, you know, next year in the same report, uh, as we get to that point on, on 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, 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 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 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 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, a model in isolation doesn't do you a whole lot of good. You've gotta have that connection with the world. And, and I think as things evolve, we will, I, I personally, my own personal belief is that world model development requires the same. You have to have feedback from the real world. To be able to 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 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 kind of get us to functioning. So there'll be some sort of analog presumably to that notion in the world model development world as we go forward.

So speaking of AI in the physical world, takeaway number 5, robots still fail at most household tasks even as they excel in controlled environments. You have any, any robots in your house, Chris?

Well, only the ones that most people have. We have the vacuum going around and such. But, you know, I keep seeing, you know, especially this is a much bigger thing in China than it is here. With, with robots in production in a lot of households. I saw something just a few days ago about a humanoid robot that, you know, is doing elderly care and washes the dishes and things like that. And that was exactly what I was wondering is like if you took that out of, of their version of CES and kind of explore it, I wonder when you have a kind of 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 I guess—

I want one.

I want one. I'm ready.

You're ready?

Yeah.

I'm 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 5 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, 80 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 of the, I forget the, so we had 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 software, to whatever, you know. And so big networks of restaurants 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 guess 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 around. 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 kind of stir-fried them or something like that. It's like, 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 see.

What your vision. 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, a certain leveling up in China above what we've done in the US and probably fairly substantial one at that.

So, is that because of safety restrictions in the US or is it—

I'm not actually sure. I think, I think that my sense is that robots have just been a higher priority for quite a long time, 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, you know, doing performances way back before 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, whereas for us we are surging here in the US, but I think we're coming from behind on the experience side of that. So it'll be interesting to see how that plays out over time.

Yeah, makes sense. Well, something that is playing out over time, takeaway number 6, responsible AI is not keeping pace with AI capabilities. Capability with safety benchmarks lagging and incidents rising sharply. Chris, obviously this one hits close to home for me. This is part of what we're hopefully helping people deal with, but it also reminded me of, you know, of course, incidents and other things that we've seen from my work with Prediction Guard, but also 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. Yeah.

You know, I'm gonna, I'm gonna make a comment that probably will surprise most people tuning in, and that is, first First of all, making it very clear, I only speak for myself and not for my employer or any other organization. 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. There are federal regulations here in the US that prohibit certain things that in the commercial space people might just surge and go do in terms of safety issues. And so 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 we're not surging the way the commercial industries have the freedom to. But I think it also— there's quite an intense focus on keeping things that need to be safe, safe. And I have noticed that. And sometimes as someone who is always enthusiastic on new technologies, I get a little bit frustrated, but then I stop and go, no, I'm glad, I'm glad we're that way. So I just thought I'd mention that.

Yeah, 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 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 AI governance towards exportable proof and 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 going to start to see some of that needed exportable proof being part of even audits and certifications, whether that's SOC 2 or AI-specific types of certifications for companies.

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

If you've been listening to the show over the past few months, you realize just just how transformative agentic AI is, whether that's Claude Code or Hermes Agent or custom-built software that you're deploying for operational efficiencies or as new products to your customers. Regardless of your maturity now, this is the world that we're headed towards, this agentic AI world. And there's a lot of security and governance teams that aren't letting these agents go into production because of risks related to agency and autonomy. And how do you take care of things like prompt injections or insecure tool usage? There's a lot to take care of, and that's why I'm personally spending my time outside of the show working with an amazing team of AI engineers to build Prediction Guard. Prediction Guard is an AI control plane that you run in your own infrastructure structure behind your firewall. Developers can build on top of this control plane using everything that they want to use—OpenAI and Anthropic compatible APIs, MCP servers, frameworks like LangChain—but all of this is plugged into a built-in governance harness that enforces your organization's AI policies. And all of that telemetry goes back to your monitoring and alerting systems. I would encourage you to check out what we're doing at predictionguard.com/practicalai. You can schedule a demo with me and the team, and I'd love to get your feedback on what we're doing. So visit us at predictionguard.com/practicalai. That's predictionguard.com/practicalai. Um, well, Chris, we are almost half-ish way through, uh, the, the, the takeaways. We might not get to all of them in detail, But number 7, the United States leads in AI investment, but its ability to attract global talent is declining. Interesting. Yes, it is interesting.

It's, I think we are, and I'm not terribly surprised by that one either as we have seen diversification and also frankly as political priorities in the United States have changed. I'll leave that to folks tuning in to, to decide kind of how they're looking at it. But I think that that has also impacted that. If you're— it's certainly hard to attract— if you're talking about global talent, it's hard to attract global talent if things that are ancillary to that, things like immigration, are a challenge. So I don't— for me, at least given the current circumstances, that wasn't a surprise to run across that. I'm hoping that, 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 80% decline just in the last year in terms of the number of AI researchers and developers moving to the US, which, yeah, yeah, to the point I just made right there, is pretty astounding.

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

Yeah, I mean, I think the US still kind of corners the market on VC-driven startup, startups that are funded by that world. And, you know, Silicon Valley still holds a special place there in terms of the VC funds that are there, you know, other places too, New York and kind of growing markets in the Midwest and that sort of thing. So I think that's going to be, that's going to hold true. The report also shows still the greatest number of companies that are are being started are starting in, 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 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.

Right. That makes perfect sense. Um, and it, you know, talking about, you know, VC and the, the adoption rate and stuff. Stuff. Um, yeah, we, we, the, the last few months, the amount of AI adoption, um, has just skyrocketed. People were, until, until 2026, maybe, you know, late second 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 improvement in terms of adoption. I really don't think I've run across anyone recently, including all age brackets. I have friends who are in their 90s who are using the tools that are out there now, mostly free, mostly free, and that's what Stanford had noted is the different free tiers, but But that has, that's been interesting to see. But I noticed that I think the one thing that may have surprised me was that Stanford had noticed that we are still in the US ranked 24th at only a 28.3% adoption. So obviously I'm not, I'm not the, you know, representative in terms of my own experience of that. What are your thoughts there?

Yeah, it's super interesting. I don't know the full economics of some of these free access 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 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 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. So I've been guilty of that myself.

Yeah. Yeah. And so, yeah, it, it will be, it will be interesting to see how that, that level of usage continues to spread. I don't under, like I say, understand some of those direct-to-consumer mechanisms as well as 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, There's something that I've noticed that I was meaning to bring into the show at the right moment anyway, and that was my mother is a technologist. She's retired, mid-'80s, did old-school AI way back, but she's been kind of re-engaging in recent times. And I noticed that she was talking to me the other night about she likes to paint and then she'll capture an image in Photoshop historically and work on it and get it just the way she balance 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 because 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, yeah. And so I just thought I wanted to bring that element back into it that not AI for all things is always the answer.

Yeah, I think that there is a distinction there, Chris, because you could look at other examples of this, right? It's— 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 for less money. With probably better results and get something that tastes great already cold. But that's not the point, right? To what you were saying, that's not why they're doing things in that way. And it'll be interesting to see what kind of, what elements also see, we see a resurgence of, I don't know, people that just want to use Excel and do analysis because they enjoy doing it. 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 going to be acceptable, right? So in 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 going to use email. That's not going to work in the world that we live in, right? Yeah.

I will tie a bow on this by saying to your example, once upon a time I took a hand at trying to brew some beer and also wine, and both my beer and my wine were terrible, but I took great pride and I drank them because I took a lot of pride in it. Whereas I I certainly wouldn't have done that in a professional context. But yes, I, yeah, there, there, it's interesting to see the human element inching back in and recognizing that there's a difference.

So you were kind of trying to bow on this, but I think one more anecdote is really interesting because one of my friends that brews their own beer, one of the things that they told me that they did recently was they they actually used their creativity in an AI tool and said, I kind of want to brew a beer that's like this and it has this ABV and like these notes and it's kind of this style. I kind of 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 recipe for that. That. And then he went to the brewing store and 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 great to us. And they got that and he brewed the beer and it worked out great. So I think actually this could be a spark to some of those hobbies on that side of things. I know you know, even just, well, I 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. And that's, you know, part of what I enjoy about doing the planning of trips or something like that. Absolutely. Well, moving on. Yeah. What are we on? We're on takeaway 9. 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, I think we've talked a lot about this on the show over the months. Yes. Yeah. There's no way you can get a junior software dev position writing SQL queries anymore.

That's, that's right. That's gone. Um, it's, it's interesting. And, and the senior-level people have, have been learning very rapidly that it is time to embrace I really— it's funny, as we went into New Year's, I knew a lot of people who were still pushing back on that. At this point, as we're recording in late May, 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 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 not a technical person, but you're using Microsoft Office and to your point a few minutes ago, a lot of the, a lot of the basic stuff, you're gonna be using an AI assistant in your tool. And I think that's going, 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, I mean, we have hired folks into our company and I'm sure I'll be part of hiring processes in the future. And it's interesting because there, you could say there's no longer the chance for the entry-level jobs, but there's still a chance to hire people in and give them, even if they're a more junior engineer, like we have, for example, in our company, maybe like we have our own repo with all the skills, quad code skills that are relevant to our stack and connect to this and that and help you get up and going. And really like that, that's a lot of power that you can give someone. Now, obviously there are debugging things and architecture things that are very take, take a very skilled person, a more senior person to get to the bottom of. But I think, 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 were on the job market, then they might have more of a chance. I definitely acknowledge that jobs will be lost here. There certainly will be, right? And that is a hard thing for many people. Yeah.

Going back before we leave this, because I think this is important, there are other things we can skip over in the interest of time, but I think a really big point there is the way we're learning things has to change too in that, and I'll share a 2 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 the tools that are out there using Claude Code 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 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 rationale? Would it make sense to do this and have that? And that, has been transforming for me, who is not fresh out of college, to, to continue to learn at a rapid pace. 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. So I, I really wanted to, to just kind of get, get out there and urge people to give it a shot.

Yeah, it does tie into one of the other takeaways, uh, 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. I I think, you know, related to what you're talking about. And yeah, it's like, I think the stat they gave is 80% of high school and college students now use AI for school-related things, but a very small percentage of teachers, you know, have any sort of policy in place around that usage or 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 going to work.

Which is the right, yeah, it's not going to 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 tool. 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 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 in middle school, uh, just now, straight A's, 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, I would urge teachers, uh, and I know you're restricted by your school policies and such, but, um, try to be open to that, uh, open to, to, to taking on the new tools 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 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 want to stick that into some type of AI thing and ask some questions and work through it. Some of the other ones that, just so people know them, are AI's environmental footprint is expanding. 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 thanks again to Stanford for continuing to do this great work.

They do great reports every year. We love— we wait for this every year to dig into and have fun with it.

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

Absolutely. See you next time.

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