WEBVTT
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Every once in a while, I have to go outside and touch grass.
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Right.
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Living in Chicago, I'm pretty much hibernating for like four months out of the year, but it was a refreshing recent trip to San Francisco and Silicon Valley that made me realize that okay, it's probably good to get out a little bit more and talk to people on the cutting edge of AI.
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And I thought it was probably a good time for a show that kind of took a look at that.
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And yes, it's maybe kind of boring to hear about some of my recent trips, but I also think it's important because I was able to meet with a lot of the smartest people in AI at some of the biggest companies building the technology of tomorrow that we'll all use.
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So, although I'm not going to be naming names or giving away any juicy details, I can't do any of that.
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What I did think that I would do on today's show is to give you five AI trends, problems, and opportunities that are around the corner.
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That maybe I had an inkling of this happening before my recent travels, but after some of these conversations, I said this is something that we have to talk about.
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So uh here's the big picture.
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I think that San Francisco and Silicon Valley is kind of a tell on what's coming next.
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I think that AI has changed a lot over the past four years from uh kind of the chat bot novelty, right?
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That open AI kicked off with Chat GPT to now it is deeply embedded in kind of running the US economy.
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And I think that there's definitely a Silicon Valley and San Francisco sticker shock.
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Uh, right.
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For me, I was kind of amazed to see that the entire city and everyone in there seemingly was just running on AI.
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And I'm seeing the same problems and opportunities, uh, kind of regardless of who I talked with.
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So that's from both technical builders and non-technical workers.
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So no matter where you fall on the spectrum, I hope that you're gonna get a little bit out of today's show.
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So, speaking of that, stick with me for the next this one's gonna be a fast one, 20-ish minutes.
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All right.
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And here's what you're gonna learn you're gonna know five AI trends from some of my recent travels that I think you should pay attention to.
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Uh, you're gonna know why.
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Well, I think you should try the impossible when it comes to AI, and I'll explain what that means.
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And I'm gonna give you the one biggest problem that's plaguing both technical people and non-technical people alike.
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All right, let's get to it.
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Welcome to Everyday AI.
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My name's Jordan Wilson, and this thing's for you.
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It's an unedited, unscripted daily live stream podcast and a free daily newsletter helping everyday business leaders like you and me keep up with all of this in uh AI innovation.
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I tell you what's important, what's not, and how you can use that information to grow your company and your career.
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Uh, so starts here, but the real cheat code is our website.
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That's your everydayai.com.
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It's also gonna be in your show notes if you're listening on the podcast.
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But there, make sure you sign up for our free daily newsletter.
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We're gonna be recapping the highlights from today's show as well as all of the other AI news that you need to know to stay ahead if you want to be the smartest person in AI in your company.
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All right, let's get to it.
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Uh live stream audience, what's going on?
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If you have any questions, uh, make sure you let me know.
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People joining from all over the globe, love to see it.
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Uh, Renee joining from Germany.
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Good morning.
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Brian from Minnesota, everyone else, good to see you.
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Uh, but yeah, if you have any questions as I'm tackling this or comments, please, please let me know.
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I I am gonna, I don't know if I can you know do the audience engagement every single time, but every once in a while, I'd love to hear uh what you all have to say.
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So it's been a busy week for me, right?
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Full disclosure, normally don't travel a lot.
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Not a big fan of it as much as I used to be.
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I just get a little bit more tired, right?
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I I find myself spending each and every day more and more time trying to increase the quality of the podcast.
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So it's not as easy as me for you know to travel and do a couple of shows on the road that I used to, but uh, I did have a great opportunity to partner with Sage more on that, uh, probably later this week on their futures conference.
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But uh while I was out in San Francisco and Silicon Valley, I got to, you know, meet with a lot of awesome people, right?
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So I met with six different people from I'll say big AI tech, a handful of others from well-known AI startups and uh other technical leaders from big enterprise companies as well.
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And then also I was uh fortunate enough to travel to St.
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Louis right after that San Francisco trip uh to keynote the uh marketing AI Summit for Worldwide Technology.
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So uh little photo of me there uh blabbing on about uh AI.
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So thanks for uh to my friends at WWT for bringing me out for that.
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Um, and you know, I put this out in the newsletter.
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So uh if this sounds like a boring show, if you're a normal podcast listener, make sure you sign up for our newsletter.
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A lot of times I'm just like, do you guys want to hear this or not?
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So you guys wanted to hear some of my takeaways uh from the past week because I was super lucky to be able to talk to a lot of smart, uh, you know, like I said, people building AI, people on the cutting edge, literally trailblazing what's coming next and building what's coming next, uh, as well as enterprise leaders from all over who are actually using this AI.
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So here's the five big takeaways.
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All right.
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Also, these are just random observations from the last week, right?
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Of great conversations.
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These aren't uh, you know, this isn't an all-encompassing list, like these are the five most important, you know, trends and problems and opportunities.
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This is just kind of what I stumbled upon through a lot of these conversations, all right.
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Number one is probably the most random, but my gosh, I was shocked.
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Autonomous cars are so much better than human drivers, all right.
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Number two, AI generalists don't really exist anymore.
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Number three, AI homework is a thing for office workers, but it shouldn't be.
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Uh, number four, faux mat is real.
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That's fear of missing agent time, and number five, AI acceleration is an opportunity and a problem for everyone.
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All right, so let's tackle it.
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Number one, autonomous cars are better than human drivers.
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That is an absolute fact, and this is one of those things, right?
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I think there's probably a couple cities where uh the Waymo's and all of these other autonomous vehicles are fairly common, right?
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I know uh San Francisco, Silicon Valley, I know Austin, Texas, I believe.
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Uh, I think in Chicago, where there's a pilot program, but it's gonna be a while.
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But I think what many of us think about AI and how it's going to change our lives, we think of just like, oh, using ChatGPT or Claude or Gemini or Copilot or whatever it is, right?
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Uh, but obviously AI is making a big impact in the real world as well.
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I notice it all the time here on the streets of Chicago, seeing the little, you know, delivery robots, uh, right.
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But I don't know.
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I'm like 50, I don't deliver food.
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I'm not actually 50, but I feel like I'm, you know, much older than I am.
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I don't really get food delivered a lot.
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So I haven't, you know, had one of those little uh, you know, robots uh knock at my door and be like, here's your dinner.
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Um, but the Waymo's okay, it's obviously a very touristy thing to do, right?
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Anyone, if you're from another city and you go to San Francisco, right?
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It's like it's probably become like a bucket list tourist thing.
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But one thing I noticed I took, I don't know, at least 15 Ubers or Lyfts over the past week, and I'm not kidding.
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Every single driver I had was terrible, absolutely terrible.
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And I'm like, how is this possible?
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Is this the norm?
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Uh I mean, I had someone go 85 miles an hour in a complete downpour on the interstate, just you know, bobbing and weaving throughout traffic.
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I'm like, okay, I don't like this.
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I I I mean, I had someone else, an older gentleman, which is fine, but stopping, you know, at night on a you know, rural highway, speed limit 55, stopping at stop signs and stop lights to like log mileage in a notebook or something, you know, and and it's like, all right, the the the light's been green for 10 seconds, there's cars coming behind, right?
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So many things like every single driver I had was just pretty bad, right?
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And this made me realize that I think autonomous AI vehicles, right, are going to really stick.
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And if you don't spend time in an area where uh you know these vehicles are, uh, it is something I think worth paying attention to.
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So, you know, Waymo did report 92% fewer serious or fatal injury crashes than comparable human driver benchmarks.
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So, like I said, embodied AI might not be on your radar right now, right?
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Delivery, uh robots, right?
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Drone deliveries, uh, you know, going from point A to point B versus a taxi or an Uber or a Lyft.
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But it's actually something after my first experience, if I ever have the option again, I'm never having a human driver, right?
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And that might sound crazy, but I mean, there's a point where there's other, you know, a uh autonomous uh vehicle companies.
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Uh, apparently, some people I talked to said that they do a lot of testing there.
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Um, you know, even those uh companies that maybe don't have full approval in other places, right, that we might not all know about.
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But I mean, there's a point where there was a Waymo in front of me.
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I was in the Waymo, there was a Waymo in front of me, a Waymo behind me, and then there was uh another autonomous uh company that I had never heard of, uh, you know, kind of ahead and to the left of me.
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And to tell you the truth, I'm like, that was probably the safest uh I had felt in a car the entire time, right?
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Like on winding roads, I think I was uh, yeah, this was just in downtown San Francisco, and I'm like, I don't want any human drivers anymore.
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All right, so that's a random takeaway, but that's takeaway number one.
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I don't know.
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Has has anyone else like feel this way?
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Is it just me, right?
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Big Bogey says autonomous vehicles 100%, nobody will even own them, just Uber and Waymo and a few others.
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Yeah, I don't know.
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I ever had the option again.
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Sorry, not taking a human driver.
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So uh if you are a human driver and you're like, oh, you know, Waymo, you're taking my job.
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Like, I don't know, let's just collectively try to be less distracted drivers.
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Yeah, not even to talk about they're all on their phones, that's that's another story.
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I feel I'm gonna like sound like a very old man complaining if I keep going down this route.
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All right.
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Uh, number two, AI generalists don't really exist anymore, which for me was interesting to see.
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Because that's something I personally have prided myself on being somewhat of an AI generalist.
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But I think even in the last six months, that's become even harder and harder.
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And I think the downside right now of AI's rapid rate of growth is there's fewer generalists.
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You know, so I think there's obviously more and more specialties in and around AI that are popping up that didn't exist three or four years ago, right?
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We're gonna see all these new uh, you know, job job roles, job types, uh, career paths that don't exist today, right?
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Um, you know, some of the more immediate ones are probably around agentic orchestration, uh, you know, uh trust and observability teams, right?
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Entire teams that are doing this.
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Uh, but it was kind of weird to see that of all the people I talked to, and you know, I'm asking them, it doesn't seem like anyone really knows AI generalists anymore.
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And I get it, because that's what I've always tried to do.
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And I think that I did a decent job at keeping up until maybe like six months ago.
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So, what is an AI generalist?
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I like to think of it as speaking multiple languages, right?
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So, even if you think of certain modalities, right?
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So, if you say, okay, uh text-to-text AI, right?
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Agentic AI, um, AI photo, AI video, AI audio, all of these different things.
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I feel I was fairly fluent in all of these different categories, maybe six months ago.
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Now it's a struggle.
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And I think that that's being seen across the industry.
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And that's kind of difficult because I think that most organizations, and I've said this before, you need people like me.
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You need people who are generalists.
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Yes, you obviously, especially at you know, the big tech companies, you need your teams that are, you know, just doing reinforcement learning with human feedback.
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You need just your teams uh that are doing deep and technical AI deployments.
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Uh, you need your uh, you know, your ML and your AI folks.
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You need all those people, but I also think you need those generalists that can be the translators and they can be the liaisons between those that are very technical, those that are deep in their wells of expertise, and then the everyday AI knowledge worker that maybe might not be very uh knowledgeable in AI, but is expected to use AI for the larger part of their day.
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I think organizations need generalists, but you know, adaptive generalists obviously, because the the field moves so fast.
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So I do think, in terms of that, you know, one uh you know, going back to one of the things I teased early on in the show, uh, you know, let me scroll back here.
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I think the one biggest problem, uh, and the one impossible thing is being an adaptive generalist in today's uh AI scene.
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It is extremely important, but becoming increasingly harder and harder to do.
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Uh, right.
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Obviously, the nature of my job and what I do does require me to have a decent and working understanding of a little bit of everything, but that's gotten even more and more difficult um as time has gone on.
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Uh, I don't know if anyone else feels that way.
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Uh if you've maybe prided yourself as being an AI generalist, but I think we're gonna see fewer and fewer of those people.
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But I think that skill set is actually gonna become increasingly more and more important um because think of it as languages again, right?
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And think of the average uh enterprise company.
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Maybe two years ago, three years ago, right, most of their AI work was done inside maybe Copilot or it was done inside of you know Google Gemini or ChatGPT as an example, right?
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Now you have teams that are in theory a little more fragmented because they're using more and more AI tools.
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But when someone has a question on how all of these pieces work together, right?
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It's like someone speaking uh Mandarin and French, and you don't have someone that speaks those languages, but the directions are in English, right?
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It is becoming more and more important.
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And I think that this is leading to lower ROI on AI because you don't have a single person that understands all of these different moving pieces and can bring them all together, right?
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This is your kind of traditional role of digital transformation, right?
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But just the pace of it and all of these new essential, like essentially these languages that are coming on at all point.
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And when you think of agentic AI in particular, that's changing.
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You know, now it's like all of a sudden there's yes, let's say there's 20 AI languages before with agentic AI.
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Now it's like adding three dialects to all of those existing languages because what is possible across all of those different quote unquote AI mediums and AI channels changes drastically with agencai AI.
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Because yes, now you are doing it some of the time, but some of the time agents are going in there and they're you know going and working with the AI audio, they're working with the AI video programs, they're working with the AI images, et cetera, stitching it all together, design, vive coding, uh, you know, agentic QA, all of these things.
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Uh yeah, I like what Jackie said here.
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She said AI generalists with specialists uh will be normal as uh in her opinion, as AI grows.
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You still need both.
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Yeah, I agree.
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It's just so difficult to find the generalists, and you are now finding more and more specialists because obviously, you know, two or three years ago, there weren't as many AI specialists, right?
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Because we were all in the same boat, you know, in 2023 and 2024.
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And then I think, you know, aside from if you had a background in machine learning, right?
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But I think everyone else was kind of in the same boat, you know, we were all in the implementation um phase in the experimentation phase in 2023, 2024.
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But I think, you know, people obviously started to niche down uh in 2025 and fast forward to 2026, there's just so many different specialties that I think they aren't able to always communicate uh or to communicate to a central person what's going on and how it impacts everyone else.
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All right.
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Uh next AI homework number three here.
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AI homework is a thing, but but it shouldn't be.
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All right, here's what I mean by that.
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I'm not talking about students, I'm not talking about in the classroom.
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Don't get me started on that.
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I'll go off on a random rant.
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I was shocked.
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And this was across almost every single company that I talked with.
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The amount of AI learning that has to be done at home if you want to keep up and if you want to get ahead.
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This issue is obviously multifactorial, right?
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One companies are just sprinting head down and you know, not properly providing uh, you know, quote unquote on the clock training.
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That's one piece of it.
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And that's not necessarily anything new from conversations that I had over the past week, but the other big facet of this is the cutting edge of agentic AI is getting understandably so more and more of the Wild West, right?
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If you thought, you know, traditional large language models in AI was the wild, wild west from 2022 to 2025.
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Yeah, agentic AI is even more so.
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But I understand it from both the employer perspective and the employee perspective, right?
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So what I saw was a lot of the smartest AI leaders that I talked to, they're experimenting at home, right?
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Whether it's, oh, you know, I you know got a new Mac Mini and I'm running OpenClaw or I'm going to get a big, you know, Mac studio so I can run all these local models.
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And after a lot of conversations, it just seems like even larger organizations, even if they set up, you know, certain uh you know, virtual sandboxes and they give employees you know an opportunity to set up you know virtual machines where they can go test all these things on the cutting edge, it's still not quite the same, right?
00:19:24.079 --> 00:19:40.240
A virtual machine, you know, things like you know, Nemo Claw from uh NVIDIA, you know, great, but most people are still wanting or absolutely needing to experiment at home or doing at-home AI homework, right?
00:19:40.480 --> 00:19:48.240
Um, knowing like, hey, we should be testing these models, you know, here internally in our workflows, right?
00:19:49.119 --> 00:19:56.720
Especially when it comes to open source models, when it comes to uh niche models that are only in certain uh aspects, right?
00:19:56.799 --> 00:20:08.559
If if you're you know doing something as an example for PDF parsing or something like that, probably don't have the time to necessarily do that because everyone's outputs, everyone's expected deliverables doesn't go down.
00:20:08.640 --> 00:20:11.680
It doesn't decrease as AI's capabilities increase.
00:20:11.839 --> 00:20:14.960
So something ultimately gets squeezed out.
00:20:15.039 --> 00:20:23.039
And what that seems to be, at least for AI leaders that are really pushing themselves, is their time at home, their time away from office, right?
00:20:23.119 --> 00:20:27.759
You have people now working probably more from home than they were before.
00:20:27.920 --> 00:20:33.200
Yes, I know you still have that subset of people that uh whose companies are not as AI capable.
00:20:33.359 --> 00:20:37.119
Those weren't really the people that I spent most of my time talking to over the last week.
00:20:37.279 --> 00:20:39.759
These are companies more on the cutting edge of AI.
00:20:39.839 --> 00:20:45.599
Uh, but you still have those people, obviously, that are able to bank a lot of their time, especially if they're in a hybrid or remote role.
00:20:45.680 --> 00:20:47.759
And those people do have time to experiment.
00:20:47.920 --> 00:21:01.200
But I was actually shocked at the kind of the seniority of a lot of these people that said I have to do so much AI work at home on my own machines, whether it's well, some things just aren't always safe.
00:21:01.359 --> 00:21:08.880
Uh, but sometimes it's just like there's not enough time uh to run these experiments with everything that I have going on.
00:21:09.039 --> 00:21:13.680
I think expectations for a lot of enterprise workers have just increased, right?
00:21:13.920 --> 00:21:20.559
Because smart AI leaders are saying, okay, you have the cutting-edge AI tools, right?
00:21:20.880 --> 00:21:26.000
So naturally, you're not getting a brand new job description, which is another problem.
00:21:26.160 --> 00:21:40.000
I think we need to start rewriting old job descriptions because AI won't take your job, but it will take your old job description if your uh company or department is not updating what it means to work, anyways.
00:21:40.160 --> 00:21:50.799
Um, it is just taking away so much time by not having that dedicated uh kind of physical sandbox to go and experiment.
00:21:51.279 --> 00:21:55.839
If I'm a business leader right now and I'm onboarding new people, right?
00:21:57.200 --> 00:21:58.880
I know budgets, right?
00:21:58.960 --> 00:21:59.759
Yeah, it's it's tough.
00:22:00.240 --> 00:22:02.480
Right, but I'm giving everyone two computers.
00:22:03.599 --> 00:22:05.119
Here's your work computer.
00:22:05.279 --> 00:22:06.960
Here's what you can and can't run.
00:22:07.119 --> 00:22:10.000
Here's the you know company approved AI.
00:22:10.400 --> 00:22:12.880
And then here's your personal computer, right?
00:22:13.279 --> 00:22:14.799
Go break this thing.
00:22:15.119 --> 00:22:17.039
Go get go get dangerous.
00:22:17.200 --> 00:22:18.160
Go get scary, right?
00:22:18.240 --> 00:22:19.839
Don't break any laws, right?
00:22:19.920 --> 00:22:21.839
But do not connect anything.
00:22:22.000 --> 00:22:27.759
Uh, you know, no work emails, no work drives, no nothing, but go here, here's a machine.
00:22:28.000 --> 00:22:30.000
It's not that expensive, right?
00:22:30.160 --> 00:22:36.799
Whether you're running local models or whether you're running something like Kodaks or Claude Code or anti-gravity or whatever it is, right?
00:22:36.960 --> 00:22:40.559
Here's your machine that you can go mess some things up with, right?
00:22:40.720 --> 00:22:44.000
Here's your at-home play one day a week, right?
00:22:44.079 --> 00:22:50.160
We want our employees spending, you know, four hours out of their day pushing this machine and capabilities to the limits.
00:22:50.240 --> 00:23:03.039
And then we bring it back on a Friday, you know, lunchtime, and we say, hey, here's things that are working that we need to now test to see if we can safely do it in production uh with our current workflows.
00:23:03.759 --> 00:23:07.039
Yeah, Angie says, not enough time and not enough memory.
00:23:07.119 --> 00:23:09.519
So we have to use our own personal subscriptions.
00:23:09.599 --> 00:23:10.000
Yeah.
00:23:10.160 --> 00:23:15.039
Uh yeah, Frank, Frank here saying, what about balancing personal uh family time with AI?
00:23:15.119 --> 00:23:16.079
It is an interesting issue.
00:23:16.160 --> 00:23:16.400
Yeah.
00:23:16.559 --> 00:23:31.279
I was surprised just with the again, people leading big companies uh in highly visible roles, you know, not just at AI labs and AI startups, but other enterprise companies, the amount of work people are doing at home.
00:23:31.440 --> 00:23:32.559
Number one, yes, I get it.
00:23:32.640 --> 00:23:33.359
It's exciting.
00:23:33.519 --> 00:23:42.240
You know, being able to build things that, you know, especially if you're mid-career, where you're like, wow, I had this idea five, 10, 15 years ago.
00:23:42.319 --> 00:23:45.119
You know, we kicked it around, you know, we couldn't do it.
00:23:45.200 --> 00:23:46.079
It was too expensive.
00:23:46.160 --> 00:23:52.240
Now, those type of projects you can literally do in an hour, right?
00:23:52.480 --> 00:23:54.720
Get an MVP demos over memos.
00:23:55.039 --> 00:23:56.480
Um, but this is huge.
00:23:56.720 --> 00:23:57.839
This is huge.
00:23:58.480 --> 00:24:06.240
Speaking of that, bleeding into our next kind of big problem and big opportunity, faux mat is real.
00:24:06.640 --> 00:24:10.319
This is the first time I talked to other people that had faux mat.
00:24:10.640 --> 00:24:14.640
All right, so obviously everyone's heard of FOMO, fear of missing out.
00:24:14.960 --> 00:24:18.880
FOMAT is fear of missing agent time.
00:24:19.440 --> 00:24:20.000
All right.
00:24:20.319 --> 00:24:25.759
So my setup might be a little extra, but I feel this is for a lot of people, right?
00:24:25.839 --> 00:24:32.240
So I have at home, I have a Mac Studio, you know, running local models, running codecs, running quad code, all those things.
00:24:32.480 --> 00:24:36.960
But I also just bought a newer, powerful laptop, right?
00:24:37.279 --> 00:24:45.839
And when I'm going around to all of these meetings, it sometimes hurt me to have to close my laptop, right?
00:24:46.000 --> 00:25:02.880
Because I'm I'm building things, I'm running, you know, deep, uh, deep agent runs, trying to, you know, unearth new things that'll help me grow my business, new opportunities, uh, you know, new uh, you know, kind of tools and you know, AI toys that I can use to do my work better.
00:25:03.039 --> 00:25:05.119
And having to close the laptop, right?
00:25:05.200 --> 00:25:12.799
For meetings, for you know, when you're going to conferences, keynotes that you're listening to or working on, that's tough.
00:25:12.960 --> 00:25:14.319
That's tough, right?
00:25:14.559 --> 00:25:19.920
Um, FOMAT, this is the first time I've ever talked to anyone else that felt the same way.
00:25:20.079 --> 00:25:23.759
Uh, so desktop AI agents are so powerful.
00:25:24.000 --> 00:25:26.480
I think it's created a new level of FOMO.
00:25:26.720 --> 00:25:35.440
So, yes, there's certain things that you can do, you know, schedule things uh on your computer, but it does have to be on, it has to be powered, right?
00:25:35.599 --> 00:25:39.119
My Mac Studio, poor thing, doesn't get to sleep, right?
00:25:39.200 --> 00:25:41.359
It's running around the clock.
00:25:41.680 --> 00:25:45.839
Every once in a while, I'll restart it so it can take a power nap, right?
00:25:45.920 --> 00:25:48.559
Um, or as my wife says, the the disco nap.
00:25:48.960 --> 00:25:55.119
Um but this was shocking to me, right?
00:25:55.200 --> 00:26:00.480
That other people understood uh and felt that way, I think was was was huge.
00:26:00.799 --> 00:26:01.119
All right.
00:26:01.279 --> 00:26:16.960
So that is, and by the way, I actually I was joking around uh about this with a couple of people I talked to about FOMAT, and I'm like, I'm just gonna build uh an app so when I close my MacBook, the MacBook thinks it's still open and it can just keep running.
00:26:17.119 --> 00:26:19.359
Uh so I actually built that, it's almost done.
00:26:19.519 --> 00:26:21.519
So I'm excited for that personally.
00:26:21.759 --> 00:26:22.319
All right.
00:26:22.480 --> 00:26:34.160
Uh, and then last, number five, uh, and this is both a huge problem and the biggest enterprise opportunity if you can understand how to close the gap.
00:26:34.319 --> 00:26:43.039
Uh, and this is just number five is AI acceleration is probably the biggest opportunity and the biggest problem for everyone.
00:26:43.599 --> 00:26:46.720
So maybe I was naive in thinking this.
00:26:46.799 --> 00:26:54.000
Uh, but I thought, oh, a lot of these people that I talk to are gonna have this thing figured out, right?
00:26:54.400 --> 00:27:05.519
They're building the most world's most powerful AI, uh, right, whether it was at the big labs, the AI startups I talk to, big enterprise, you know, uh enterprise workers at huge companies.
00:27:05.680 --> 00:27:09.279
I'm like, okay, these conversations are gonna be great because they have the answers.
00:27:09.440 --> 00:27:14.640
They're building it, and I'm gonna be able to come and find these secret answers and share them with everyone.
00:27:14.799 --> 00:27:15.440
Guess what?
00:27:15.599 --> 00:27:22.880
Even for the people building all of the AI that we use, the acceleration is a huge problem.
00:27:23.119 --> 00:27:34.000
And I was both uh relieved, yet also curious that even when I didn't ask people, they all said the same thing.
00:27:34.160 --> 00:27:40.400
They said, Hey, I've only been feeling this over the past six months up until you know late 2025.
00:27:40.720 --> 00:27:47.519
Uh, you know, it was easier or at least manageable to keep up with the pace and the rate of AI.
00:27:47.599 --> 00:27:50.880
And obviously, that has to do with agentic AI.
00:27:50.960 --> 00:27:51.519
Uh, right.
00:27:51.599 --> 00:27:58.000
So I think Anthropic really kind of kicked this off, you know, with Claude Code, with Claude Cowork.
00:27:58.160 --> 00:28:08.720
But also, you know, we started to hear at the end of 2025, you know, the big AI labs essentially start to admit, right, that their new models were built by a bigger version, right?
00:28:08.799 --> 00:28:16.480
So you can make the argument whether that's uh, you know, recursive self-improvement or not, uh, you know, or is it just distillation, right?
00:28:16.640 --> 00:28:18.559
Big model creating other models, right?
00:28:18.640 --> 00:28:37.519
But when you have someone like anthropic saying, oh, you know, Claude co-work was 100% built by Claude Code, uh, right, or our anthropic models, uh, right, that means a new pace and a new rate of AI acceleration that most people, number one, weren't expecting.
00:28:37.759 --> 00:28:39.680
Number two, it hit everyone.
00:28:39.839 --> 00:28:44.400
And number three, like I said, it is both probably one of the biggest problems and one of the biggest opportunities.
00:28:44.559 --> 00:28:46.480
So, what is the opportunity there?
00:28:46.799 --> 00:28:56.319
Well, the opportunity is the knowledge gap that I think is widely acknowledged, and most people understand now that it's a problem.
00:28:56.960 --> 00:29:11.759
That gap between capabilities and what the everyday worker is actually using AI for, that capability gap is only going to grow because of the education, the lack of education, the lack of training, learning, and development.
00:29:11.920 --> 00:29:19.440
You know, let's just say everyone's baseline AI understanding across an enterprise, you know, goes up five to 10% per quarter, right?
00:29:19.680 --> 00:29:23.359
Which normal digital transformation, if you're looking at that, you're happy with it.
00:29:23.519 --> 00:29:23.839
Right.
00:29:24.000 --> 00:29:35.359
Those are um kind of knowledge growth rates that during traditional uh, you know, phases of um technical innovation, you'd be happy with.
00:29:36.079 --> 00:29:49.039
But if AI's capabilities, right, you can not say we're looking at Moore's Law or anything like that, but AI's capabilities are sometimes doubling by the year or nearly doubling by the quarter, right?
00:29:49.200 --> 00:29:57.279
So that gap between what people are using AI for and what is possible, that gap is sizing up.
00:29:57.440 --> 00:29:59.920
It's sizing up week over week, month over month.
00:30:00.160 --> 00:30:02.640
And it is hitting everyone, right?
00:30:02.720 --> 00:30:13.519
Even the big AI companies that are building this technology are seeing that it is becoming increasingly difficult when everyone has a quote unquote job to do.
00:30:13.839 --> 00:30:22.160
Not everyone can be kind of like me and you know, testing every single tool, every single model update, every single mode, right?
00:30:22.319 --> 00:30:23.200
You can't do it.
00:30:23.279 --> 00:30:25.599
So naturally, that means that that gap is widening.
00:30:25.680 --> 00:30:33.440
So Gardner actually projects worldwide AI spending will hit 2.5 trillion uh in 2026, up 44%.
00:30:34.079 --> 00:30:41.920
So increasing spending 50% year over year on a technology like AI is not a normal growth rate.
00:30:42.160 --> 00:30:42.640
All right.
00:30:42.960 --> 00:30:48.720
So uh Microsoft actually they just came out with their work trend index study this morning.
00:30:48.880 --> 00:30:53.039
All right, it's one of my favorite uh and I think one of the best studies around AI.
00:30:53.119 --> 00:30:55.359
So we'll be sharing that in today's newsletter, FYI.
00:30:55.440 --> 00:31:03.680
Uh, but they just said that Microsoft says active uh 365 agents grew 15x year over year, right?
00:31:04.240 --> 00:31:07.279
When it comes to capabilities in agentic AI, right?
00:31:07.359 --> 00:31:15.200
As as AI starts to get arms and legs and a brain and it's going out in the real world and it has the ability to act now, right?
00:31:15.359 --> 00:31:16.960
AI is very much a two-way street.
00:31:17.039 --> 00:31:19.920
Whereas before, you know, AI could kind of only read.
00:31:20.079 --> 00:31:26.400
Now it can read, write, and it can take actions on our behalf around the clock 24-7 with agenc.
00:31:27.200 --> 00:31:43.039
The capability gap is growing and acceleration, acceleration, I think it actually becomes dangerous because those capabilities obviously outpace knowledge, learning, and development, but they also outpace governance and guardrails, right?
00:31:43.279 --> 00:31:46.559
Most AI labs aren't releasing new mods.
00:31:46.720 --> 00:31:55.039
Yes, they come out with safety cards, and you know, if if a model goes off the rails and tradings, right, during red teaming, they'll share those things.
00:31:55.359 --> 00:32:14.160
But there's so many new problems and threats that come out with new models that not only is the world maybe technically not ready for, and we've seen that a lot more with you know, the the mythos, which I think might be as much marketing uh as it is, you know, real powerful model.
00:32:14.240 --> 00:32:24.799
Uh, anyways, you have new models, whether it's on cyber or other capabilities, um, that do kind of pose a threat, right?
00:32:25.359 --> 00:32:37.279
And I think we always focus on what the models can do that we want them to, and not necessarily think about, you know, I I think I called it like agentic crash, right?
00:32:37.359 --> 00:32:44.079
Uh, what happens when agents go off the rails because they are too smart and they are, well, too ahead of the rails.
00:32:44.160 --> 00:32:46.559
And, you know, they might think you give them a goal.
00:32:46.720 --> 00:32:49.119
Hey, here's the goal, here's the guardrails.
00:32:49.200 --> 00:33:00.079
They might justify it to themselves, well, it maybe makes sense for me to go outside of these preset guardrails because it helps me accomplish the goal, not knowing what ramifications uh that might have.
00:33:00.720 --> 00:33:01.359
All right.
00:33:02.319 --> 00:33:03.839
That's it, y'all.
00:33:04.319 --> 00:33:06.640
I hope this was kind of a fun episode.
00:33:06.720 --> 00:33:08.799
It was fun for me to think about and plan.
00:33:08.880 --> 00:33:12.000
Uh, but that's just kind of what I see as coming next.
00:33:12.240 --> 00:33:15.839
Five AI trends, problems, and opportunities around the corner.
00:33:16.000 --> 00:33:18.240
So uh, if this was helpful, let me know.
00:33:18.400 --> 00:33:22.960
If it wasn't helpful, sign up for the newsletter and you know, vote for something else, you know.
00:33:23.119 --> 00:33:26.880
Every week I try to put you guys in control for at least one episode.
00:33:26.960 --> 00:33:33.119
Uh, you know, because I do my best to try to stay up to date and say, hey, here's what I think you know people need to hear.
00:33:33.279 --> 00:33:36.000
But every once in a while I say, what do you want to hear?
00:33:36.079 --> 00:33:36.880
What do you want to learn?
00:33:36.960 --> 00:33:40.079
So make sure if you haven't already, please go to your everydayai.com.
00:33:40.160 --> 00:33:41.599
Sign up for the free daily newsletter.
00:33:41.680 --> 00:33:42.319
Thanks for tuning in.
00:33:42.480 --> 00:33:45.519
Hope to see you back tomorrow and every day for more everyday AI.
00:33:45.599 --> 00:33:46.640
Thanks, y'all.
00:33:48.960 --> 00:33:52.319
And that's a wrap for today's edition of Everyday AI.
00:33:52.480 --> 00:33:53.599
Thanks for joining us.
00:33:53.759 --> 00:33:57.519
If you enjoyed this episode, please subscribe and leave us a rating.
00:33:57.680 --> 00:33:59.279
It helps keep us going.
00:33:59.519 --> 00:34:06.640
For a little more AI magic, visit your everydayai.com and sign up to our daily newsletter so you don't get left behind.
00:34:06.799 --> 00:34:09.440
Go break some barriers, and we'll see you next time.
00:00:02.160 --> 00:00:06.320
Every once in a while, I have to go outside and touch grass.
00:00:06.559 --> 00:00:06.959
Right.
00:00:07.120 --> 00:00:27.280
Living in Chicago, I'm pretty much hibernating for like four months out of the year, but it was a refreshing recent trip to San Francisco and Silicon Valley that made me realize that okay, it's probably good to get out a little bit more and talk to people on the cutting edge of AI.
00:00:27.679 --> 00:00:33.679
And I thought it was probably a good time for a show that kind of took a look at that.
00:00:33.840 --> 00:00:49.520
And yes, it's maybe kind of boring to hear about some of my recent trips, but I also think it's important because I was able to meet with a lot of the smartest people in AI at some of the biggest companies building the technology of tomorrow that we'll all use.
00:00:49.679 --> 00:00:55.679
So, although I'm not going to be naming names or giving away any juicy details, I can't do any of that.
00:00:55.920 --> 00:01:04.319
What I did think that I would do on today's show is to give you five AI trends, problems, and opportunities that are around the corner.
00:01:04.400 --> 00:01:15.200
That maybe I had an inkling of this happening before my recent travels, but after some of these conversations, I said this is something that we have to talk about.
00:01:15.439 --> 00:01:18.000
So uh here's the big picture.
00:01:18.239 --> 00:01:23.920
I think that San Francisco and Silicon Valley is kind of a tell on what's coming next.
00:01:24.079 --> 00:01:31.120
I think that AI has changed a lot over the past four years from uh kind of the chat bot novelty, right?
00:01:31.200 --> 00:01:37.920
That open AI kicked off with Chat GPT to now it is deeply embedded in kind of running the US economy.
00:01:38.159 --> 00:01:43.439
And I think that there's definitely a Silicon Valley and San Francisco sticker shock.
00:01:43.519 --> 00:01:44.079
Uh, right.
00:01:44.319 --> 00:01:53.760
For me, I was kind of amazed to see that the entire city and everyone in there seemingly was just running on AI.
00:01:53.840 --> 00:02:00.959
And I'm seeing the same problems and opportunities, uh, kind of regardless of who I talked with.
00:02:01.120 --> 00:02:05.439
So that's from both technical builders and non-technical workers.
00:02:05.599 --> 00:02:10.639
So no matter where you fall on the spectrum, I hope that you're gonna get a little bit out of today's show.
00:02:10.800 --> 00:02:14.960
So, speaking of that, stick with me for the next this one's gonna be a fast one, 20-ish minutes.
00:02:15.039 --> 00:02:15.360
All right.
00:02:15.520 --> 00:02:21.360
And here's what you're gonna learn you're gonna know five AI trends from some of my recent travels that I think you should pay attention to.
00:02:21.520 --> 00:02:22.879
Uh, you're gonna know why.
00:02:23.039 --> 00:02:28.000
Well, I think you should try the impossible when it comes to AI, and I'll explain what that means.
00:02:28.080 --> 00:02:35.280
And I'm gonna give you the one biggest problem that's plaguing both technical people and non-technical people alike.
00:02:35.840 --> 00:02:37.680
All right, let's get to it.
00:02:37.759 --> 00:02:38.879
Welcome to Everyday AI.
00:02:38.960 --> 00:02:41.120
My name's Jordan Wilson, and this thing's for you.
00:02:41.199 --> 00:02:51.120
It's an unedited, unscripted daily live stream podcast and a free daily newsletter helping everyday business leaders like you and me keep up with all of this in uh AI innovation.
00:02:51.280 --> 00:02:56.400
I tell you what's important, what's not, and how you can use that information to grow your company and your career.
00:02:56.560 --> 00:03:00.080
Uh, so starts here, but the real cheat code is our website.
00:03:00.159 --> 00:03:02.240
That's your everydayai.com.
00:03:02.319 --> 00:03:04.719
It's also gonna be in your show notes if you're listening on the podcast.
00:03:04.800 --> 00:03:07.599
But there, make sure you sign up for our free daily newsletter.
00:03:07.680 --> 00:03:17.439
We're gonna be recapping the highlights from today's show as well as all of the other AI news that you need to know to stay ahead if you want to be the smartest person in AI in your company.
00:03:17.680 --> 00:03:19.840
All right, let's get to it.
00:03:20.000 --> 00:03:22.080
Uh live stream audience, what's going on?
00:03:22.159 --> 00:03:25.039
If you have any questions, uh, make sure you let me know.
00:03:25.120 --> 00:03:27.599
People joining from all over the globe, love to see it.
00:03:27.759 --> 00:03:30.719
Uh, Renee joining from Germany.
00:03:30.960 --> 00:03:31.919
Good morning.
00:03:32.240 --> 00:03:35.599
Brian from Minnesota, everyone else, good to see you.
00:03:35.680 --> 00:03:40.639
Uh, but yeah, if you have any questions as I'm tackling this or comments, please, please let me know.
00:03:40.800 --> 00:03:48.960
I I am gonna, I don't know if I can you know do the audience engagement every single time, but every once in a while, I'd love to hear uh what you all have to say.
00:03:49.199 --> 00:03:52.639
So it's been a busy week for me, right?
00:03:52.800 --> 00:03:54.960
Full disclosure, normally don't travel a lot.
00:03:55.120 --> 00:03:57.439
Not a big fan of it as much as I used to be.
00:03:57.599 --> 00:03:59.680
I just get a little bit more tired, right?
00:03:59.759 --> 00:04:04.719
I I find myself spending each and every day more and more time trying to increase the quality of the podcast.
00:04:04.800 --> 00:04:16.560
So it's not as easy as me for you know to travel and do a couple of shows on the road that I used to, but uh, I did have a great opportunity to partner with Sage more on that, uh, probably later this week on their futures conference.
00:04:16.720 --> 00:04:22.800
But uh while I was out in San Francisco and Silicon Valley, I got to, you know, meet with a lot of awesome people, right?
00:04:22.879 --> 00:04:33.040
So I met with six different people from I'll say big AI tech, a handful of others from well-known AI startups and uh other technical leaders from big enterprise companies as well.
00:04:33.199 --> 00:04:36.240
And then also I was uh fortunate enough to travel to St.
00:04:36.399 --> 00:04:42.959
Louis right after that San Francisco trip uh to keynote the uh marketing AI Summit for Worldwide Technology.
00:04:43.120 --> 00:04:47.600
So uh little photo of me there uh blabbing on about uh AI.
00:04:47.680 --> 00:04:51.839
So thanks for uh to my friends at WWT for bringing me out for that.
00:04:52.079 --> 00:04:54.240
Um, and you know, I put this out in the newsletter.
00:04:54.319 --> 00:04:59.279
So uh if this sounds like a boring show, if you're a normal podcast listener, make sure you sign up for our newsletter.
00:04:59.360 --> 00:05:01.360
A lot of times I'm just like, do you guys want to hear this or not?
00:05:01.519 --> 00:05:21.839
So you guys wanted to hear some of my takeaways uh from the past week because I was super lucky to be able to talk to a lot of smart, uh, you know, like I said, people building AI, people on the cutting edge, literally trailblazing what's coming next and building what's coming next, uh, as well as enterprise leaders from all over who are actually using this AI.
00:05:21.920 --> 00:05:23.839
So here's the five big takeaways.
00:05:23.920 --> 00:05:24.399
All right.
00:05:24.720 --> 00:05:29.839
Also, these are just random observations from the last week, right?
00:05:29.920 --> 00:05:31.120
Of great conversations.
00:05:31.279 --> 00:05:38.319
These aren't uh, you know, this isn't an all-encompassing list, like these are the five most important, you know, trends and problems and opportunities.
00:05:38.480 --> 00:05:41.839
This is just kind of what I stumbled upon through a lot of these conversations, all right.
00:05:42.000 --> 00:05:45.519
Number one is probably the most random, but my gosh, I was shocked.
00:05:45.680 --> 00:05:49.199
Autonomous cars are so much better than human drivers, all right.
00:05:49.360 --> 00:05:53.199
Number two, AI generalists don't really exist anymore.
00:05:53.439 --> 00:06:00.399
Number three, AI homework is a thing for office workers, but it shouldn't be.
00:06:00.560 --> 00:06:03.279
Uh, number four, faux mat is real.
00:06:03.360 --> 00:06:12.319
That's fear of missing agent time, and number five, AI acceleration is an opportunity and a problem for everyone.
00:06:13.120 --> 00:06:16.319
All right, so let's tackle it.
00:06:18.560 --> 00:06:24.079
Number one, autonomous cars are better than human drivers.
00:06:24.160 --> 00:06:27.519
That is an absolute fact, and this is one of those things, right?
00:06:27.759 --> 00:06:35.279
I think there's probably a couple cities where uh the Waymo's and all of these other autonomous vehicles are fairly common, right?
00:06:35.439 --> 00:06:39.920
I know uh San Francisco, Silicon Valley, I know Austin, Texas, I believe.
00:06:40.079 --> 00:06:44.240
Uh, I think in Chicago, where there's a pilot program, but it's gonna be a while.
00:06:44.399 --> 00:06:54.399
But I think what many of us think about AI and how it's going to change our lives, we think of just like, oh, using ChatGPT or Claude or Gemini or Copilot or whatever it is, right?
00:06:54.480 --> 00:06:58.480
Uh, but obviously AI is making a big impact in the real world as well.
00:06:58.639 --> 00:07:04.160
I notice it all the time here on the streets of Chicago, seeing the little, you know, delivery robots, uh, right.
00:07:04.319 --> 00:07:05.040
But I don't know.
00:07:05.120 --> 00:07:07.120
I'm like 50, I don't deliver food.
00:07:07.199 --> 00:07:10.480
I'm not actually 50, but I feel like I'm, you know, much older than I am.
00:07:10.560 --> 00:07:12.319
I don't really get food delivered a lot.
00:07:12.399 --> 00:07:18.879
So I haven't, you know, had one of those little uh, you know, robots uh knock at my door and be like, here's your dinner.
00:07:19.120 --> 00:07:25.519
Um, but the Waymo's okay, it's obviously a very touristy thing to do, right?
00:07:25.600 --> 00:07:28.959
Anyone, if you're from another city and you go to San Francisco, right?
00:07:29.040 --> 00:07:32.639
It's like it's probably become like a bucket list tourist thing.
00:07:32.800 --> 00:07:42.560
But one thing I noticed I took, I don't know, at least 15 Ubers or Lyfts over the past week, and I'm not kidding.
00:07:43.040 --> 00:07:48.319
Every single driver I had was terrible, absolutely terrible.
00:07:48.399 --> 00:07:50.160
And I'm like, how is this possible?
00:07:50.319 --> 00:07:51.439
Is this the norm?
00:07:51.839 --> 00:08:04.639
Uh I mean, I had someone go 85 miles an hour in a complete downpour on the interstate, just you know, bobbing and weaving throughout traffic.
00:08:04.800 --> 00:08:06.879
I'm like, okay, I don't like this.
00:08:07.040 --> 00:08:29.600
I I I mean, I had someone else, an older gentleman, which is fine, but stopping, you know, at night on a you know, rural highway, speed limit 55, stopping at stop signs and stop lights to like log mileage in a notebook or something, you know, and and it's like, all right, the the the light's been green for 10 seconds, there's cars coming behind, right?
00:08:29.759 --> 00:08:36.799
So many things like every single driver I had was just pretty bad, right?
00:08:37.679 --> 00:08:46.559
And this made me realize that I think autonomous AI vehicles, right, are going to really stick.
00:08:46.720 --> 00:08:55.360
And if you don't spend time in an area where uh you know these vehicles are, uh, it is something I think worth paying attention to.
00:08:55.519 --> 00:09:03.519
So, you know, Waymo did report 92% fewer serious or fatal injury crashes than comparable human driver benchmarks.
00:09:03.679 --> 00:09:09.519
So, like I said, embodied AI might not be on your radar right now, right?
00:09:09.679 --> 00:09:12.399
Delivery, uh robots, right?
00:09:12.720 --> 00:09:21.759
Drone deliveries, uh, you know, going from point A to point B versus a taxi or an Uber or a Lyft.
00:09:21.919 --> 00:09:30.399
But it's actually something after my first experience, if I ever have the option again, I'm never having a human driver, right?
00:09:30.480 --> 00:09:38.240
And that might sound crazy, but I mean, there's a point where there's other, you know, a uh autonomous uh vehicle companies.
00:09:38.320 --> 00:09:41.840
Uh, apparently, some people I talked to said that they do a lot of testing there.
00:09:42.080 --> 00:09:51.519
Um, you know, even those uh companies that maybe don't have full approval in other places, right, that we might not all know about.
00:09:52.480 --> 00:09:55.360
But I mean, there's a point where there was a Waymo in front of me.
00:09:55.440 --> 00:10:04.720
I was in the Waymo, there was a Waymo in front of me, a Waymo behind me, and then there was uh another autonomous uh company that I had never heard of, uh, you know, kind of ahead and to the left of me.
00:10:04.799 --> 00:10:13.039
And to tell you the truth, I'm like, that was probably the safest uh I had felt in a car the entire time, right?
00:10:13.120 --> 00:10:22.879
Like on winding roads, I think I was uh, yeah, this was just in downtown San Francisco, and I'm like, I don't want any human drivers anymore.
00:10:23.039 --> 00:10:26.000
All right, so that's a random takeaway, but that's takeaway number one.
00:10:26.159 --> 00:10:26.559
I don't know.
00:10:26.639 --> 00:10:29.200
Has has anyone else like feel this way?
00:10:29.360 --> 00:10:30.320
Is it just me, right?
00:10:30.480 --> 00:10:36.799
Big Bogey says autonomous vehicles 100%, nobody will even own them, just Uber and Waymo and a few others.
00:10:36.879 --> 00:10:37.919
Yeah, I don't know.
00:10:38.080 --> 00:10:39.440
I ever had the option again.
00:10:39.679 --> 00:10:41.840
Sorry, not taking a human driver.
00:10:42.000 --> 00:10:48.159
So uh if you are a human driver and you're like, oh, you know, Waymo, you're taking my job.
00:10:48.240 --> 00:10:52.240
Like, I don't know, let's just collectively try to be less distracted drivers.
00:10:52.320 --> 00:10:56.480
Yeah, not even to talk about they're all on their phones, that's that's another story.
00:10:56.639 --> 00:11:00.960
I feel I'm gonna like sound like a very old man complaining if I keep going down this route.
00:11:01.519 --> 00:11:02.080
All right.
00:11:02.240 --> 00:11:14.240
Uh, number two, AI generalists don't really exist anymore, which for me was interesting to see.
00:11:15.519 --> 00:11:22.480
Because that's something I personally have prided myself on being somewhat of an AI generalist.
00:11:23.039 --> 00:11:29.120
But I think even in the last six months, that's become even harder and harder.
00:11:29.279 --> 00:11:36.240
And I think the downside right now of AI's rapid rate of growth is there's fewer generalists.
00:11:36.559 --> 00:11:46.720
You know, so I think there's obviously more and more specialties in and around AI that are popping up that didn't exist three or four years ago, right?
00:11:46.879 --> 00:11:55.759
We're gonna see all these new uh, you know, job job roles, job types, uh, career paths that don't exist today, right?
00:11:55.919 --> 00:12:03.679
Um, you know, some of the more immediate ones are probably around agentic orchestration, uh, you know, uh trust and observability teams, right?
00:12:03.840 --> 00:12:05.679
Entire teams that are doing this.
00:12:05.840 --> 00:12:18.799
Uh, but it was kind of weird to see that of all the people I talked to, and you know, I'm asking them, it doesn't seem like anyone really knows AI generalists anymore.
00:12:19.039 --> 00:12:22.879
And I get it, because that's what I've always tried to do.
00:12:23.039 --> 00:12:27.519
And I think that I did a decent job at keeping up until maybe like six months ago.
00:12:27.600 --> 00:12:29.360
So, what is an AI generalist?
00:12:29.440 --> 00:12:32.399
I like to think of it as speaking multiple languages, right?
00:12:32.480 --> 00:12:35.679
So, even if you think of certain modalities, right?
00:12:35.840 --> 00:12:40.000
So, if you say, okay, uh text-to-text AI, right?
00:12:40.240 --> 00:12:46.639
Agentic AI, um, AI photo, AI video, AI audio, all of these different things.
00:12:48.960 --> 00:12:54.000
I feel I was fairly fluent in all of these different categories, maybe six months ago.
00:12:54.159 --> 00:12:55.519
Now it's a struggle.
00:12:55.600 --> 00:12:58.320
And I think that that's being seen across the industry.
00:12:58.480 --> 00:13:05.919
And that's kind of difficult because I think that most organizations, and I've said this before, you need people like me.
00:13:06.000 --> 00:13:07.440
You need people who are generalists.
00:13:07.519 --> 00:13:14.799
Yes, you obviously, especially at you know, the big tech companies, you need your teams that are, you know, just doing reinforcement learning with human feedback.
00:13:14.879 --> 00:13:18.879
You need just your teams uh that are doing deep and technical AI deployments.
00:13:18.960 --> 00:13:22.399
Uh, you need your uh, you know, your ML and your AI folks.
00:13:22.480 --> 00:13:44.799
You need all those people, but I also think you need those generalists that can be the translators and they can be the liaisons between those that are very technical, those that are deep in their wells of expertise, and then the everyday AI knowledge worker that maybe might not be very uh knowledgeable in AI, but is expected to use AI for the larger part of their day.
00:13:44.960 --> 00:13:52.159
I think organizations need generalists, but you know, adaptive generalists obviously, because the the field moves so fast.
00:13:52.320 --> 00:14:01.759
So I do think, in terms of that, you know, one uh you know, going back to one of the things I teased early on in the show, uh, you know, let me scroll back here.
00:14:01.919 --> 00:14:12.000
I think the one biggest problem, uh, and the one impossible thing is being an adaptive generalist in today's uh AI scene.
00:14:12.080 --> 00:14:16.960
It is extremely important, but becoming increasingly harder and harder to do.
00:14:17.120 --> 00:14:17.919
Uh, right.
00:14:18.159 --> 00:14:32.639
Obviously, the nature of my job and what I do does require me to have a decent and working understanding of a little bit of everything, but that's gotten even more and more difficult um as time has gone on.
00:14:32.720 --> 00:14:35.759
Uh, I don't know if anyone else feels that way.
00:14:36.080 --> 00:14:45.440
Uh if you've maybe prided yourself as being an AI generalist, but I think we're gonna see fewer and fewer of those people.
00:14:45.600 --> 00:14:54.080
But I think that skill set is actually gonna become increasingly more and more important um because think of it as languages again, right?
00:14:54.159 --> 00:14:56.639
And think of the average uh enterprise company.
00:14:56.879 --> 00:15:08.639
Maybe two years ago, three years ago, right, most of their AI work was done inside maybe Copilot or it was done inside of you know Google Gemini or ChatGPT as an example, right?
00:15:08.720 --> 00:15:14.799
Now you have teams that are in theory a little more fragmented because they're using more and more AI tools.
00:15:14.960 --> 00:15:19.440
But when someone has a question on how all of these pieces work together, right?
00:15:19.519 --> 00:15:28.879
It's like someone speaking uh Mandarin and French, and you don't have someone that speaks those languages, but the directions are in English, right?
00:15:29.039 --> 00:15:30.720
It is becoming more and more important.
00:15:30.799 --> 00:15:43.120
And I think that this is leading to lower ROI on AI because you don't have a single person that understands all of these different moving pieces and can bring them all together, right?
00:15:43.200 --> 00:15:47.679
This is your kind of traditional role of digital transformation, right?
00:15:47.840 --> 00:15:53.840
But just the pace of it and all of these new essential, like essentially these languages that are coming on at all point.
00:15:54.159 --> 00:15:58.399
And when you think of agentic AI in particular, that's changing.
00:15:58.559 --> 00:16:05.279
You know, now it's like all of a sudden there's yes, let's say there's 20 AI languages before with agentic AI.
00:16:05.440 --> 00:16:18.000
Now it's like adding three dialects to all of those existing languages because what is possible across all of those different quote unquote AI mediums and AI channels changes drastically with agencai AI.
00:16:18.080 --> 00:16:34.799
Because yes, now you are doing it some of the time, but some of the time agents are going in there and they're you know going and working with the AI audio, they're working with the AI video programs, they're working with the AI images, et cetera, stitching it all together, design, vive coding, uh, you know, agentic QA, all of these things.
00:16:35.519 --> 00:16:37.519
Uh yeah, I like what Jackie said here.
00:16:37.600 --> 00:16:44.480
She said AI generalists with specialists uh will be normal as uh in her opinion, as AI grows.
00:16:44.559 --> 00:16:45.120
You still need both.
00:16:45.200 --> 00:16:45.919
Yeah, I agree.
00:16:46.000 --> 00:16:56.320
It's just so difficult to find the generalists, and you are now finding more and more specialists because obviously, you know, two or three years ago, there weren't as many AI specialists, right?
00:16:56.399 --> 00:17:00.639
Because we were all in the same boat, you know, in 2023 and 2024.
00:17:00.799 --> 00:17:05.200
And then I think, you know, aside from if you had a background in machine learning, right?
00:17:05.359 --> 00:17:14.079
But I think everyone else was kind of in the same boat, you know, we were all in the implementation um phase in the experimentation phase in 2023, 2024.
00:17:14.240 --> 00:17:30.240
But I think, you know, people obviously started to niche down uh in 2025 and fast forward to 2026, there's just so many different specialties that I think they aren't able to always communicate uh or to communicate to a central person what's going on and how it impacts everyone else.
00:17:30.720 --> 00:17:31.359
All right.
00:17:31.759 --> 00:17:35.039
Uh next AI homework number three here.
00:17:35.200 --> 00:17:37.599
AI homework is a thing, but but it shouldn't be.
00:17:37.920 --> 00:17:39.279
All right, here's what I mean by that.
00:17:39.680 --> 00:17:43.359
I'm not talking about students, I'm not talking about in the classroom.
00:17:43.680 --> 00:17:44.720
Don't get me started on that.
00:17:44.799 --> 00:17:46.640
I'll go off on a random rant.
00:17:47.599 --> 00:17:48.559
I was shocked.
00:17:49.119 --> 00:17:52.960
And this was across almost every single company that I talked with.
00:17:53.519 --> 00:18:00.559
The amount of AI learning that has to be done at home if you want to keep up and if you want to get ahead.
00:18:02.400 --> 00:18:05.359
This issue is obviously multifactorial, right?
00:18:05.599 --> 00:18:14.000
One companies are just sprinting head down and you know, not properly providing uh, you know, quote unquote on the clock training.
00:18:14.160 --> 00:18:15.359
That's one piece of it.
00:18:15.519 --> 00:18:32.400
And that's not necessarily anything new from conversations that I had over the past week, but the other big facet of this is the cutting edge of agentic AI is getting understandably so more and more of the Wild West, right?
00:18:32.480 --> 00:18:38.480
If you thought, you know, traditional large language models in AI was the wild, wild west from 2022 to 2025.
00:18:38.640 --> 00:18:41.200
Yeah, agentic AI is even more so.
00:18:41.519 --> 00:18:47.839
But I understand it from both the employer perspective and the employee perspective, right?
00:18:48.000 --> 00:18:56.799
So what I saw was a lot of the smartest AI leaders that I talked to, they're experimenting at home, right?
00:18:56.960 --> 00:19:05.839
Whether it's, oh, you know, I you know got a new Mac Mini and I'm running OpenClaw or I'm going to get a big, you know, Mac studio so I can run all these local models.
00:19:05.920 --> 00:19:24.000
And after a lot of conversations, it just seems like even larger organizations, even if they set up, you know, certain uh you know, virtual sandboxes and they give employees you know an opportunity to set up you know virtual machines where they can go test all these things on the cutting edge, it's still not quite the same, right?
00:19:24.079 --> 00:19:40.240
A virtual machine, you know, things like you know, Nemo Claw from uh NVIDIA, you know, great, but most people are still wanting or absolutely needing to experiment at home or doing at-home AI homework, right?
00:19:40.480 --> 00:19:48.240
Um, knowing like, hey, we should be testing these models, you know, here internally in our workflows, right?
00:19:49.119 --> 00:19:56.720
Especially when it comes to open source models, when it comes to uh niche models that are only in certain uh aspects, right?
00:19:56.799 --> 00:20:08.559
If if you're you know doing something as an example for PDF parsing or something like that, probably don't have the time to necessarily do that because everyone's outputs, everyone's expected deliverables doesn't go down.
00:20:08.640 --> 00:20:11.680
It doesn't decrease as AI's capabilities increase.
00:20:11.839 --> 00:20:14.960
So something ultimately gets squeezed out.
00:20:15.039 --> 00:20:23.039
And what that seems to be, at least for AI leaders that are really pushing themselves, is their time at home, their time away from office, right?
00:20:23.119 --> 00:20:27.759
You have people now working probably more from home than they were before.
00:20:27.920 --> 00:20:33.200
Yes, I know you still have that subset of people that uh whose companies are not as AI capable.
00:20:33.359 --> 00:20:37.119
Those weren't really the people that I spent most of my time talking to over the last week.
00:20:37.279 --> 00:20:39.759
These are companies more on the cutting edge of AI.
00:20:39.839 --> 00:20:45.599
Uh, but you still have those people, obviously, that are able to bank a lot of their time, especially if they're in a hybrid or remote role.
00:20:45.680 --> 00:20:47.759
And those people do have time to experiment.
00:20:47.920 --> 00:21:01.200
But I was actually shocked at the kind of the seniority of a lot of these people that said I have to do so much AI work at home on my own machines, whether it's well, some things just aren't always safe.
00:21:01.359 --> 00:21:08.880
Uh, but sometimes it's just like there's not enough time uh to run these experiments with everything that I have going on.
00:21:09.039 --> 00:21:13.680
I think expectations for a lot of enterprise workers have just increased, right?
00:21:13.920 --> 00:21:20.559
Because smart AI leaders are saying, okay, you have the cutting-edge AI tools, right?
00:21:20.880 --> 00:21:26.000
So naturally, you're not getting a brand new job description, which is another problem.
00:21:26.160 --> 00:21:40.000
I think we need to start rewriting old job descriptions because AI won't take your job, but it will take your old job description if your uh company or department is not updating what it means to work, anyways.
00:21:40.160 --> 00:21:50.799
Um, it is just taking away so much time by not having that dedicated uh kind of physical sandbox to go and experiment.
00:21:51.279 --> 00:21:55.839
If I'm a business leader right now and I'm onboarding new people, right?
00:21:57.200 --> 00:21:58.880
I know budgets, right?
00:21:58.960 --> 00:21:59.759
Yeah, it's it's tough.
00:22:00.240 --> 00:22:02.480
Right, but I'm giving everyone two computers.
00:22:03.599 --> 00:22:05.119
Here's your work computer.
00:22:05.279 --> 00:22:06.960
Here's what you can and can't run.
00:22:07.119 --> 00:22:10.000
Here's the you know company approved AI.
00:22:10.400 --> 00:22:12.880
And then here's your personal computer, right?
00:22:13.279 --> 00:22:14.799
Go break this thing.
00:22:15.119 --> 00:22:17.039
Go get go get dangerous.
00:22:17.200 --> 00:22:18.160
Go get scary, right?
00:22:18.240 --> 00:22:19.839
Don't break any laws, right?
00:22:19.920 --> 00:22:21.839
But do not connect anything.
00:22:22.000 --> 00:22:27.759
Uh, you know, no work emails, no work drives, no nothing, but go here, here's a machine.
00:22:28.000 --> 00:22:30.000
It's not that expensive, right?
00:22:30.160 --> 00:22:36.799
Whether you're running local models or whether you're running something like Kodaks or Claude Code or anti-gravity or whatever it is, right?
00:22:36.960 --> 00:22:40.559
Here's your machine that you can go mess some things up with, right?
00:22:40.720 --> 00:22:44.000
Here's your at-home play one day a week, right?
00:22:44.079 --> 00:22:50.160
We want our employees spending, you know, four hours out of their day pushing this machine and capabilities to the limits.
00:22:50.240 --> 00:23:03.039
And then we bring it back on a Friday, you know, lunchtime, and we say, hey, here's things that are working that we need to now test to see if we can safely do it in production uh with our current workflows.
00:23:03.759 --> 00:23:07.039
Yeah, Angie says, not enough time and not enough memory.
00:23:07.119 --> 00:23:09.519
So we have to use our own personal subscriptions.
00:23:09.599 --> 00:23:10.000
Yeah.
00:23:10.160 --> 00:23:15.039
Uh yeah, Frank, Frank here saying, what about balancing personal uh family time with AI?
00:23:15.119 --> 00:23:16.079
It is an interesting issue.
00:23:16.160 --> 00:23:16.400
Yeah.
00:23:16.559 --> 00:23:31.279
I was surprised just with the again, people leading big companies uh in highly visible roles, you know, not just at AI labs and AI startups, but other enterprise companies, the amount of work people are doing at home.
00:23:31.440 --> 00:23:32.559
Number one, yes, I get it.
00:23:32.640 --> 00:23:33.359
It's exciting.
00:23:33.519 --> 00:23:42.240
You know, being able to build things that, you know, especially if you're mid-career, where you're like, wow, I had this idea five, 10, 15 years ago.
00:23:42.319 --> 00:23:45.119
You know, we kicked it around, you know, we couldn't do it.
00:23:45.200 --> 00:23:46.079
It was too expensive.
00:23:46.160 --> 00:23:52.240
Now, those type of projects you can literally do in an hour, right?
00:23:52.480 --> 00:23:54.720
Get an MVP demos over memos.
00:23:55.039 --> 00:23:56.480
Um, but this is huge.
00:23:56.720 --> 00:23:57.839
This is huge.
00:23:58.480 --> 00:24:06.240
Speaking of that, bleeding into our next kind of big problem and big opportunity, faux mat is real.
00:24:06.640 --> 00:24:10.319
This is the first time I talked to other people that had faux mat.
00:24:10.640 --> 00:24:14.640
All right, so obviously everyone's heard of FOMO, fear of missing out.
00:24:14.960 --> 00:24:18.880
FOMAT is fear of missing agent time.
00:24:19.440 --> 00:24:20.000
All right.
00:24:20.319 --> 00:24:25.759
So my setup might be a little extra, but I feel this is for a lot of people, right?
00:24:25.839 --> 00:24:32.240
So I have at home, I have a Mac Studio, you know, running local models, running codecs, running quad code, all those things.
00:24:32.480 --> 00:24:36.960
But I also just bought a newer, powerful laptop, right?
00:24:37.279 --> 00:24:45.839
And when I'm going around to all of these meetings, it sometimes hurt me to have to close my laptop, right?
00:24:46.000 --> 00:25:02.880
Because I'm I'm building things, I'm running, you know, deep, uh, deep agent runs, trying to, you know, unearth new things that'll help me grow my business, new opportunities, uh, you know, new uh, you know, kind of tools and you know, AI toys that I can use to do my work better.
00:25:03.039 --> 00:25:05.119
And having to close the laptop, right?
00:25:05.200 --> 00:25:12.799
For meetings, for you know, when you're going to conferences, keynotes that you're listening to or working on, that's tough.
00:25:12.960 --> 00:25:14.319
That's tough, right?
00:25:14.559 --> 00:25:19.920
Um, FOMAT, this is the first time I've ever talked to anyone else that felt the same way.
00:25:20.079 --> 00:25:23.759
Uh, so desktop AI agents are so powerful.
00:25:24.000 --> 00:25:26.480
I think it's created a new level of FOMO.
00:25:26.720 --> 00:25:35.440
So, yes, there's certain things that you can do, you know, schedule things uh on your computer, but it does have to be on, it has to be powered, right?
00:25:35.599 --> 00:25:39.119
My Mac Studio, poor thing, doesn't get to sleep, right?
00:25:39.200 --> 00:25:41.359
It's running around the clock.
00:25:41.680 --> 00:25:45.839
Every once in a while, I'll restart it so it can take a power nap, right?
00:25:45.920 --> 00:25:48.559
Um, or as my wife says, the the disco nap.
00:25:48.960 --> 00:25:55.119
Um but this was shocking to me, right?
00:25:55.200 --> 00:26:00.480
That other people understood uh and felt that way, I think was was was huge.
00:26:00.799 --> 00:26:01.119
All right.
00:26:01.279 --> 00:26:16.960
So that is, and by the way, I actually I was joking around uh about this with a couple of people I talked to about FOMAT, and I'm like, I'm just gonna build uh an app so when I close my MacBook, the MacBook thinks it's still open and it can just keep running.
00:26:17.119 --> 00:26:19.359
Uh so I actually built that, it's almost done.
00:26:19.519 --> 00:26:21.519
So I'm excited for that personally.
00:26:21.759 --> 00:26:22.319
All right.
00:26:22.480 --> 00:26:34.160
Uh, and then last, number five, uh, and this is both a huge problem and the biggest enterprise opportunity if you can understand how to close the gap.
00:26:34.319 --> 00:26:43.039
Uh, and this is just number five is AI acceleration is probably the biggest opportunity and the biggest problem for everyone.
00:26:43.599 --> 00:26:46.720
So maybe I was naive in thinking this.
00:26:46.799 --> 00:26:54.000
Uh, but I thought, oh, a lot of these people that I talk to are gonna have this thing figured out, right?
00:26:54.400 --> 00:27:05.519
They're building the most world's most powerful AI, uh, right, whether it was at the big labs, the AI startups I talk to, big enterprise, you know, uh enterprise workers at huge companies.
00:27:05.680 --> 00:27:09.279
I'm like, okay, these conversations are gonna be great because they have the answers.
00:27:09.440 --> 00:27:14.640
They're building it, and I'm gonna be able to come and find these secret answers and share them with everyone.
00:27:14.799 --> 00:27:15.440
Guess what?
00:27:15.599 --> 00:27:22.880
Even for the people building all of the AI that we use, the acceleration is a huge problem.
00:27:23.119 --> 00:27:34.000
And I was both uh relieved, yet also curious that even when I didn't ask people, they all said the same thing.
00:27:34.160 --> 00:27:40.400
They said, Hey, I've only been feeling this over the past six months up until you know late 2025.
00:27:40.720 --> 00:27:47.519
Uh, you know, it was easier or at least manageable to keep up with the pace and the rate of AI.
00:27:47.599 --> 00:27:50.880
And obviously, that has to do with agentic AI.
00:27:50.960 --> 00:27:51.519
Uh, right.
00:27:51.599 --> 00:27:58.000
So I think Anthropic really kind of kicked this off, you know, with Claude Code, with Claude Cowork.
00:27:58.160 --> 00:28:08.720
But also, you know, we started to hear at the end of 2025, you know, the big AI labs essentially start to admit, right, that their new models were built by a bigger version, right?
00:28:08.799 --> 00:28:16.480
So you can make the argument whether that's uh, you know, recursive self-improvement or not, uh, you know, or is it just distillation, right?
00:28:16.640 --> 00:28:18.559
Big model creating other models, right?
00:28:18.640 --> 00:28:37.519
But when you have someone like anthropic saying, oh, you know, Claude co-work was 100% built by Claude Code, uh, right, or our anthropic models, uh, right, that means a new pace and a new rate of AI acceleration that most people, number one, weren't expecting.
00:28:37.759 --> 00:28:39.680
Number two, it hit everyone.
00:28:39.839 --> 00:28:44.400
And number three, like I said, it is both probably one of the biggest problems and one of the biggest opportunities.
00:28:44.559 --> 00:28:46.480
So, what is the opportunity there?
00:28:46.799 --> 00:28:56.319
Well, the opportunity is the knowledge gap that I think is widely acknowledged, and most people understand now that it's a problem.
00:28:56.960 --> 00:29:11.759
That gap between capabilities and what the everyday worker is actually using AI for, that capability gap is only going to grow because of the education, the lack of education, the lack of training, learning, and development.
00:29:11.920 --> 00:29:19.440
You know, let's just say everyone's baseline AI understanding across an enterprise, you know, goes up five to 10% per quarter, right?
00:29:19.680 --> 00:29:23.359
Which normal digital transformation, if you're looking at that, you're happy with it.
00:29:23.519 --> 00:29:23.839
Right.
00:29:24.000 --> 00:29:35.359
Those are um kind of knowledge growth rates that during traditional uh, you know, phases of um technical innovation, you'd be happy with.
00:29:36.079 --> 00:29:49.039
But if AI's capabilities, right, you can not say we're looking at Moore's Law or anything like that, but AI's capabilities are sometimes doubling by the year or nearly doubling by the quarter, right?
00:29:49.200 --> 00:29:57.279
So that gap between what people are using AI for and what is possible, that gap is sizing up.
00:29:57.440 --> 00:29:59.920
It's sizing up week over week, month over month.
00:30:00.160 --> 00:30:02.640
And it is hitting everyone, right?
00:30:02.720 --> 00:30:13.519
Even the big AI companies that are building this technology are seeing that it is becoming increasingly difficult when everyone has a quote unquote job to do.
00:30:13.839 --> 00:30:22.160
Not everyone can be kind of like me and you know, testing every single tool, every single model update, every single mode, right?
00:30:22.319 --> 00:30:23.200
You can't do it.
00:30:23.279 --> 00:30:25.599
So naturally, that means that that gap is widening.
00:30:25.680 --> 00:30:33.440
So Gardner actually projects worldwide AI spending will hit 2.5 trillion uh in 2026, up 44%.
00:30:34.079 --> 00:30:41.920
So increasing spending 50% year over year on a technology like AI is not a normal growth rate.
00:30:42.160 --> 00:30:42.640
All right.
00:30:42.960 --> 00:30:48.720
So uh Microsoft actually they just came out with their work trend index study this morning.
00:30:48.880 --> 00:30:53.039
All right, it's one of my favorite uh and I think one of the best studies around AI.
00:30:53.119 --> 00:30:55.359
So we'll be sharing that in today's newsletter, FYI.
00:30:55.440 --> 00:31:03.680
Uh, but they just said that Microsoft says active uh 365 agents grew 15x year over year, right?
00:31:04.240 --> 00:31:07.279
When it comes to capabilities in agentic AI, right?
00:31:07.359 --> 00:31:15.200
As as AI starts to get arms and legs and a brain and it's going out in the real world and it has the ability to act now, right?
00:31:15.359 --> 00:31:16.960
AI is very much a two-way street.
00:31:17.039 --> 00:31:19.920
Whereas before, you know, AI could kind of only read.
00:31:20.079 --> 00:31:26.400
Now it can read, write, and it can take actions on our behalf around the clock 24-7 with agenc.
00:31:27.200 --> 00:31:43.039
The capability gap is growing and acceleration, acceleration, I think it actually becomes dangerous because those capabilities obviously outpace knowledge, learning, and development, but they also outpace governance and guardrails, right?
00:31:43.279 --> 00:31:46.559
Most AI labs aren't releasing new mods.
00:31:46.720 --> 00:31:55.039
Yes, they come out with safety cards, and you know, if if a model goes off the rails and tradings, right, during red teaming, they'll share those things.
00:31:55.359 --> 00:32:14.160
But there's so many new problems and threats that come out with new models that not only is the world maybe technically not ready for, and we've seen that a lot more with you know, the the mythos, which I think might be as much marketing uh as it is, you know, real powerful model.
00:32:14.240 --> 00:32:24.799
Uh, anyways, you have new models, whether it's on cyber or other capabilities, um, that do kind of pose a threat, right?
00:32:25.359 --> 00:32:37.279
And I think we always focus on what the models can do that we want them to, and not necessarily think about, you know, I I think I called it like agentic crash, right?
00:32:37.359 --> 00:32:44.079
Uh, what happens when agents go off the rails because they are too smart and they are, well, too ahead of the rails.
00:32:44.160 --> 00:32:46.559
And, you know, they might think you give them a goal.
00:32:46.720 --> 00:32:49.119
Hey, here's the goal, here's the guardrails.
00:32:49.200 --> 00:33:00.079
They might justify it to themselves, well, it maybe makes sense for me to go outside of these preset guardrails because it helps me accomplish the goal, not knowing what ramifications uh that might have.
00:33:00.720 --> 00:33:01.359
All right.
00:33:02.319 --> 00:33:03.839
That's it, y'all.
00:33:04.319 --> 00:33:06.640
I hope this was kind of a fun episode.
00:33:06.720 --> 00:33:08.799
It was fun for me to think about and plan.
00:33:08.880 --> 00:33:12.000
Uh, but that's just kind of what I see as coming next.
00:33:12.240 --> 00:33:15.839
Five AI trends, problems, and opportunities around the corner.
00:33:16.000 --> 00:33:18.240
So uh, if this was helpful, let me know.
00:33:18.400 --> 00:33:22.960
If it wasn't helpful, sign up for the newsletter and you know, vote for something else, you know.
00:33:23.119 --> 00:33:26.880
Every week I try to put you guys in control for at least one episode.
00:33:26.960 --> 00:33:33.119
Uh, you know, because I do my best to try to stay up to date and say, hey, here's what I think you know people need to hear.
00:33:33.279 --> 00:33:36.000
But every once in a while I say, what do you want to hear?
00:33:36.079 --> 00:33:36.880
What do you want to learn?
00:33:36.960 --> 00:33:40.079
So make sure if you haven't already, please go to your everydayai.com.
00:33:40.160 --> 00:33:41.599
Sign up for the free daily newsletter.
00:33:41.680 --> 00:33:42.319
Thanks for tuning in.
00:33:42.480 --> 00:33:45.519
Hope to see you back tomorrow and every day for more everyday AI.
00:33:45.599 --> 00:33:46.640
Thanks, y'all.
00:33:48.960 --> 00:33:52.319
And that's a wrap for today's edition of Everyday AI.
00:33:52.480 --> 00:33:53.599
Thanks for joining us.
00:33:53.759 --> 00:33:57.519
If you enjoyed this episode, please subscribe and leave us a rating.
00:33:57.680 --> 00:33:59.279
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00:33:59.519 --> 00:34:06.640
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00:34:06.799 --> 00:34:09.440
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