EP 222: The Dispersion of AI Jobs Across the U.S. - Why it matters

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When we talk about AI jobs, normally we just think of AI taking our jobs. It's a very pessimistic viewpoint anytime we combine the word AI and jobs. But that's not necessarily how it is right now in the real world. You know, there's something that's happening behind the scenes. You know, a lot of us think, you know, AI jobs, they're only in Silicon Valley, but that's not the case. They're actually being dispersed all across the United States. And today's show is one I'm very excited about. Being a data geek and someone that follows AI in the job market all the time. We're going to be talking about the dispersion of AI jobs across across the United States and why it matters. All right, so I'm extremely excited to get that started. But first, just as a reminder, if you're listening on the podcast, thank you. We appreciate it. As always, check out your show notes. You can always contact me, email connect on LinkedIn, but there will be the URL for our website, your everyday AI dot com. Make sure you sign up for that every single day. We break down our podcast interview in greater depth and put that in our free daily newsletter. So make sure you go sign up for that. And I tell people our website is a free generative AI university now. More than 210 past episodes. No matter what you want to learn, whether it's AI and sales, AI and entrepreneurship, we've probably already talked to five or 10 experts, so you can go learn there and go read all of our old newsletters as well. All right, so let's start the show as we do every time, with going over the AI news. All right, first, Microsoft is challenging the New York Times lawsuit in its first public response to the December lawsuit from the New York Times. So in December, the New York Times filed a lawsuit for copyright infringement against OpenAI and Microsoft. And Microsoft finally fired back and filed a motion to dismiss the case. So Microsoft is challenging the New York Times lawsuit and saying, kind of comparing it to, I love this. They compared it to the vcr from the 80s, and essentially, you know, so Microsoft compared the New York Times lawsuit to Hollywood's resistance to the VCR, which was created in the 70s and allowed users to record television programs. So they kind of use this comparison to highlight what they said was the false narrative presented by the New York Times. Regarding the impact on OpenAI's ChatGPT on the news business. All right, speaking of OpenAI, a lot of OpenAI news today. So OpenAI also first publicly responded to Elon Musk's lawsuit against the company, which we just talked about this yesterday on the Everyday AI show. But OpenAI released a statement on its website sharing excerpts of previous emails between high ranking members of the company and Elon Musk. So the statements highlighted Elon's previous support for raising funds even as a nonprofit and increasing secrecy, even though now Elon is criticizing the company for being, quote, super closed source. All right, so a couple things to know here. Elon and OpenAI were at odds over the company structure and the decision to be become a for profit entity. And originally Elon had supported raising hundreds of millions of dollars for the company, but also later proposed merging OpenAI with Tesla so the two of them could combine, which is now kind of what they're doing, but with Microsoft. And OpenAI has obviously faced criticism and investigations over its own pretty complicated government governance, which we talked about yesterday being a nonprofit, but actually having multiple entities underneath. All right, and last but not least, does Claude 3 really beat GPT4? All right, so Anthropic released Cloud Claude 3 earlier this week, its newest model. But an AI researcher from Berkeley recently noted that the test that Anthropic shared appeared to compare GPT4's original public model with Claude and not its updated and more powerful GPT4 Turbo. So while Anthropic's model initially appeared, its Opus model initially appeared to be the top performer in all of these benchmarks. Further analysis now shows that OpenAI's GPT4 Turbo model still reigns supreme in areas where they're able to benchmark it. All right, also we'll be diving more into Claude 3 tomorrow and doing some real time comparisons. So make sure to join us. All right, that's enough for the AI news. As always, you can get more at our website. But today and right now, that's not we're talking about, we're talking about the dispersion of AI jobs across the US and why it matters. All right, so it's not just me today actually have two experts. So we have a treat for y' all interviewing two people today. So let's go ahead and bring our guests on the show. There we go. And let me do a quick introduction. We have Anil Gupta, the Michael Dingman Chair in Strategy at the Business School at the University of Maryland, and Evan Schnidman, the co founder and CEO of Outrigger Group. Both of y', all, thanks for joining us. Anil, could you tell us a little bit, could you tell us a little bit about what you do?

Yeah.

So you know, as you said, so I'm a professor of business strategy, so that's my kind of the foundation. But on top of that, really, I work at the interface of business strategy, technology and entrepreneurship. And then of course, looking at how all of this is playing out on the global stage.

All right, and then, Evan, thank you as well for joining us. Can you tell us a little bit what you do as the co founder and CEO of Outrigger Group?

Yeah, absolutely. And thanks for having us here, Jordan. I have a background originally as an academic. I was a game theorist by training and ended up delving into NLP in the early days, developing a novel tool to analyze market moving language and built a fintech company based on that. So I sold that company a few years ago and now work with early and growth stage companies as a fractional executive, helping them scale quickly and really build the next generation of data and AI companies.

Perfect. All right, so let's go to the big picture here. So we've shared this in our newsletter before, but for our live stream audience, maybe you haven't seen this. So I'm going to throw this on the screen for our podcast audience. We're going to try to do our best to go ahead and describe what we have here going on, but maybe Anil, if you could walk us through. So you guys just recently released this new UMD link up AI Maps. Right. So if you are joining us on the podcast, so much data here on my screen that we're sharing. It's all live, but tell us a little bit about what this project does and kind of what we're even seeing here on our screen.

Yeah, So I mean, Jordan, what this project does is that because we started by saying that obviously AI is supremely important and we know that it's affecting every industry, every function in every industry. But what we don't have is what is happening in terms of job creation. Because ultimately it's people, it's professionals, it's, you know, others in companies that have to use the AI. So we wanted to see what's happening in terms of the creation of AI jobs across geographies, across sectors. And we felt that there was nothing. It's not like there was something that was not good enough, but there was nothing. So in some sense this is a service to the community. Sort of like Johns Hopkins Covid Map and what we do is it's updated every month, so it's live data and we create monthly updates, we create white papers, we create ranking sheets. And again, everything is. There's nothing behind a paywall.

And, and I, I love that, you know, because this is, I think, a public service to the community. Right. Because there is so much confusion about what, you know, AI jobs are doing. Right. A lot of people say, oh, are they just going to, you know, replace traditional jobs? Are they, you know, going to be new jobs that are in maybe new sectors that didn't exist before? But. But maybe. Evan, could you walk us through a little bit? How, how did this project come to be? Maybe give us a little background on how you and Aneel met and kind of what was the reason for building this project that we're looking at now. And hey, if you are curious, we'll have this in the newsletter, but it's AIMaps AI. So, Evan, walk us through kind of the creation of this project.

Yeah, absolutely. So Anil and I had the great fortune of meeting at a conference at Stanford, I think it was in 2019, so shortly before the COVID pandemic, and really bonded over the question of, you know, really how is it that we see companies enacting AI, right? What is it that that really is unique about the way different companies in different sectors are using AI and how do we know if they're lying to us? So how much of it is just marketing budget being thrown at the right buzzwords versus are they actually invest in developing AI technology, implementing AI technology, changing the way that their business operates, exploiting those efficiencies, but also staffing up and generating things that will be the engine of growth for the future. And as we continued that conversation, it became really apparent to both of us that we needed labor market data that the most. The easiest way to tell whether a company was actually investing in AI was, was whether they have AI personnel. Do they have the people to actually build and implement these technologies? And so we reached out to the team at Linkup. I'm very close to the head of strategy there, John Norberg, who I think has been integral in this project. John's been an amazing partner for both Aneel and I in building this out. And so Link up was gracious enough to supply the underlying job market data here and really allowed us to. To tap into not only what's going on geographically, but broken down by sector and each individual company.

And here's. Okay, here's a great question from Douglas. Thanks for this. And if you are joining us live, we appreciate it. Feel free to get your questions in now. So Douglas here asking, can you define what is covered as an AI job? That's a great question, because I feel even the definition is changing all the time. So, yeah, maybe let's go into a background of all these plots that are up here on the graph. What is actually covered as an AI job?

Yeah, so, I mean, what we define an AI job is a job that requires some technical skills that are integral to AI. So it's a little bit like if I use the analogy of, say, PowerPoint, that you and I use PowerPoint, but we are not the developers of PowerPoint, so therefore, we don't have the technical skills for that particular tool. So similarly, so we define an AI job because, I mean, it's like that way you can see if you use an iPhone and you use Siri or you use Alexa, you are an AI user. But that doesn't make your job or my job an AI job.

And one thing that I like, and maybe let's just get to the point of the dispersion of AI jobs, but one thing I really like about this map is it's interactive, right? So you can hover your mouse over something and see how many different, you know, AI jobs are in each different state. We have a, you know, kind of a map here of the continental United States as well as different, you know, side categories, you know, with job growth, jobs intensity, etc. So one thing, you know, I think a lot of people assume is almost all AI jobs are probably in California. And although California does, you know, it looks like as of now has about, you know, 2,234 AI jobs. Some of your main findings are showing it's not just California where all of these AI jobs are popping up. Could. Could either of you talk a little bit about this dispersion and how it's not just all concentrated in one area?

Evan?

Yeah, yeah, yeah. Happy to jump in on this. So I think so, Jordan, you highlighted that California is number one in. In number of AI jobs. But let's not forget California is also the largest state in the country, so we would expect it to have the most jobs here. The big surprise to us when we started digging into the data was actually the number of jobs in the Washington, D.C. area. Both Virginia and Maryland rank high, and in fact, Virginia is very high on that list. And I think that, you know, I think neither Anil nor I had any expectation that that was the case. And Anil, I guess I'LL let you speak to this because you actually live in that area.

Yeah, right, right, right. And I've been living here for 40 years. But I did not expect. But part of the reason then I was saying, why did I not expect? And so what happened is that as our data tells us that the rise of the D.C. region as the second largest AI hub from a jobs point of view in the United States is actually a story from 2018 onwards. So it's a relatively new story. And what happened really, like 2017 back in China, there was the event Google DeepMind's AlphaGo beat Kier at the game of Go. And that was the Sputnik moment for China. Within two months, they rolled out a national plan to become a global power in AI. Vladimir Putin said, whoever rules the AI will rule the world. And of course, that got the AI arms race going. And then Eric Schmidt, Google Chairman, Alphabet Chairman, he then became a key advisor to the Department of Defense, the Defense Innovation Board that he chaired. And he has been relentless in pushing the DoD to embrace AI. And so that's in terms of everything that goes on inside DoD, the various intelligence agencies, the equipment that they buy, the consulting services that they buy, all of that. And obviously this is a big defense ecosystem. And now this defense EcoSystem is hugely AI infused, AI embedded. There is an exception, of course. For example, we got Capital One. So Capital One is a power in terms of it's not the biggest bank, but it has more AI jobs than even JP Morgan. So you got that, you got Amazon's HQ2. So there are those. So this is kind of, it's all.

A recent story and a great question here and very, very timely from Liz. So Liz is asking, are there any specific industries or sectors where AI growth is expected to, to be particularly significant? So, you know, I know we're not, you know, I'm not asking Neil or Evan to be, you know, economic forecasters here, but where is the data telling us right now? Because we can, you know, kind of also toggle this map by sector. So what are we seeing from a sectoral standpoint in terms of where AI jobs are going? And maybe something that maybe caught one of you two off guard when it came to sectors.

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So in terms of sectors, the big sectors of course is, you know, where there is lots of data. I mean think of essentially from a sector point of view, a, which are the big sectors, which are the big employers and number two, which are the very data intensive sectors. And so naturally finance would jump up, correct? Not a surprise. You look at banking, you look at insurance, but then another sector that jumps up that you would not normally expect, that I did not normally expect until you look at the data is retail. Okay. And retail, for example, I mean Walmart, Target, they're investing a huge chunk of capital, if you will, in, in hiring AI talent. And the entire digital efforts, pretty much the entire digital efforts at Walmart are actually based in Silicon Valley, not in Bentonville. And then what's happening as AI becomes diffused across different sectors, that not every company or even every large company is necessarily hiring their own AI people. So from a sector point of view, the single biggest growth we found is in companies like Deloitte and Accenture and Boozel and Hamilton and the like. So these are the consulting firms which have grown like crazy, but not just grown like crazy in terms of their total staff, but grown like crazy in terms of the AI talent.

Yeah, sorry, no, I would add to that. I think one of the other interesting things that shakes out when we look at it on a sector by sector basis is the bifurcation within some sectors. Right. There are certain sectors that, where. So just to point to specific companies here, if you look at the difference between John Deere and Caterpillar, John Deere has rapidly staffed up on AI personnel versus Caterpillar has. Right. And that's. They've just taken different tax within the same industrial sector there, right there, the heavy equipment space. And one company really focused in on that and another one didn't. And, and so we're seeing some of that bifurcation within sector as well. And I think that's as Anil alluded to. The more data intensive sectors tend to be, you know, everybody is involved in implementing AI in one way or another, whereas the sectors where there's a little bit less data to play with, there tends to be a bifurcation between companies that are viewing AI as the future and ones that are saying, you know, we don't see that happening anytime soon.

Yeah, yeah, that's interesting. So I'm wondering, you know, I think this is great, a great resource. This, you know, UMD Link up AI Maps tool is a great resource for individuals who are curious about, you know, how AI jobs are being implemented, you know, across different industries, across different, different sectors, geographically, etc. How can companies use this data? Right. Or, you know, maybe if you even have, you know, an example of, you know, how a company has, or how a company could. But how can companies use this type of data to better prepare, you know, their near term future for hiring the right type of talent?

So at the level of companies, of course, I mean, we, on the map, AI maps, we have some data pertaining to companies, but then we don't have, you know, for example, John Deere, you know, that Evan was talking about, that John Deere couldn't go to this map and get data directly about them or about Caterpillar. So we don't, you know, because we had to make some choices about what kind of data we put out. Right. Because otherwise, you know, it would be just, you know, trillions of bits and bytes.

Yeah. And I guess something that's even important. You know, maybe we should have talked about this at the top of the show. This, this tool that you, that you guys had put together is, is the first of its kind. Right. So like, even with that, you know, maybe Evan, could you, could you talk about maybe some of the initial response? Because I think that this is something that obviously a lot of people are interested in. You've been getting, you know, some, some press around this. I believe you guys were just in the Wall Street Journal. What was the initial response to all of this data? Because it is something that so many people are talking about, but it is the world's first. So what was that initial response like?

Yeah, so I think, you know, couple of, a couple of pieces here. So first and foremost is probably to remind everybody this is free, right? We really built this as an, initially, as an, as a service to the community. And so our view has always been, you know, can we add knowledge to the community. And so we initially just launched a white paper just, just to explain what we've done here, explain the geographic findings and what. I think we've been pleasantly surprised by the fact that Bloomberg and the Wall Street Journal and a number of other publications have picked this up and really run with it. In particular keying in on not just the geographic dispersion story, but specifically on the Washington D.C. component. You know, I think everybody expects to see AI jobs popping up in the Bay Area, in Austin, Texas, in New York, in Boston. But I think to see that, that, you know, the D.C. area not only is out punching its weight, but out punching all of those except for the Bay Area. Right. Has been the key story that, that I think folks have really pointed to. I also think that the other big story is, is the question of. We've obviously the labor market has taken a turn here for, for the vast majority of tech jobs over the course of the last 18 to 24 months. And so we've seen a decline in job openings across the IT sector. But what we've actually seen is, is a rise in AI jobs recently. And so, you know, we saw that massive hiring boom in 2021, and that was true across all IT jobs. Right. Across all technology roles, including AI. Now we're seeing a dispersion, we're seeing that widening gap between AI jobs and IT jobs.

Yeah. Just to build on what Evan was saying is that December 2022 or late November 2022, as we know, was the AI shock. That's the launch of ChatGPT and then GPT4 and so on. And so we compared December 22nd was the low point in terms of the number of AI jobs because there had been companies that got themselves, they had overstaffed and so on. But then compared to December 2022 till January 2024, IT jobs are more than 20% less in terms of job postings. AI jobs is more than 40% more. So that's the growing gulf. Of course, the total number of IT jobs is going to be significantly larger than the number of AI jobs. But in terms of the growth rates, I mean, day in and day out, you read that, okay, Microsoft or Amazon or this company or that company that they're laying off people, but then they are shifting people like Apple from the car project to the AI team. So you see this kind of a widening gulf. So one, you know, of course, Andreessen, Mark Andreessen, a 16C. Software eats the world. One could add on top of that, maybe AI eats software.

That's that's a very interesting point, Anil, you know, bringing up all of these, you know, tech layoffs that, you know, have really been unfortunately booming, you know, in the first part of 2024 here. But are you, are we maybe seeing a shift in. Right. And maybe this is later down the road going back to the original PowerPoint analogy, is it going to be the point where soon most jobs might be considered AI jobs as AI becomes an integral part of our day to day lives. Right. Where the average person may be working in customer service is going to be leveraging, you know, prompting and you know, maybe Microsoft copilot. Right. Are we going to see that soon? And how can you track that with the data? Right. You know, as you are able to look historically month over month, when you get these new updates with fresh data, how can you track if AI is ultimately replacing jobs which a lot of people are scared of, versus is it just being slowly integrated into all types of roles?

Yeah. So I mean the, our data, you know, I mean the clear answer to your question would really be to look at total employment in the U.S. employment, unemployment, total number of jobs being posted. Because eventually what's going to happen is that as you said, every job becomes an AI job. It's like every job today already is an electricity job, it is an Internet job, it is a smartphone job. But you know, but of course, you know, and then the way to track that is really to look at total employment. So therefore what we are interested in is more like, you know, the AI skills because ultimately, you know, I look at the sort of a value chain. So you've got science, then you got technology, then you've got applications of that technology into product services and processes. And then of course the users and what's happening is that you had the science, then you had the technology coming out of companies such as OpenAI and Google and the like. But now the dispersion story is really the infusion of that technology, the deployment of that technology into all types of product services and processes. And that's how every job is going to become an AI job. But then the other part of the story is that do companies like John Deere, companies like Capital One, companies like Walmart, they're going to need, even as every job becomes an AI job, do they need technical AI skills?

And I'll add to that there's another element here where it's not just the technical AI skills, it's the underlying data that needs to be used to train the AI models. And so there's A whole suite of jobs around the data ecosystem. And the analogy I've been using for the last year now is if AI is the engine, then data is the fuel. And if you want to fly a fighter plane, you can't do that on low test gasoline. You need some pretty high end jet fuel. So you're going to need high quality data if you want to do high end AI work. And I think that's been a bit of a gap thus far is the idea that more data can supplant quality data. And now we're starting to move into the world of the higher quality niche data that's being used to train custom LLMs to do retrieval, augmented generation, to really be able to make these tools specialized for each individual use case. That becomes a data question as much as it's an AI question.

Yeah, Evan, Evan, that's a great point. And you know, data obviously is at the heart of everything. Not just for, you know, the, I guess the future of being able to use this data from the map for your project, but even for AI itself. Right. Data is so important. So we have, we have a lot of questions. Maybe we can do a quick little rapid fire, so to speak, so we don't actually accidentally keep you all for two hours. But, but a great question here from Monica. How do you see the UMD Linkup AI Maps project evolving?

Yeah, so I mean as we move forward, you know, we are going to be looking at questions we haven't yet so far looked at. Just to give you some sense. Number one is look at different sectors because this map currently we looked at geographies, so we look at sectors. Second, we look at what's happening in terms of the within AI jobs. What, what types of AI skills like computer vision skills, language skills, other types of skills. So in terms of what types of skills in what sectors, in what geographies. Another thing is that we have already started looking at what's the picture globally because LinkUp has data on job postings around the world. And so right now it's just the U.S. what we did, but we want to look at what's happening in Europe, what's happening in Japan, what's happening in India. We have data on China, but it's not as comprehensive as we have data on many other big economies.

Anil, I think when people use this and it is just at AIMAPS AI we'll have in the newsletter, I think you all will really love this tool. I've been enjoying using it. Evan, maybe you can take this one from Julie joining us on YouTube. Thanks for the question, Julie. So asking how could universities use this data to build the new AI workforce? It's a great question. Evan, what are your thoughts on that?

Yeah, I mean, I think there's a lot of ways for universities to not only leverage this data, but a lot of other tools in the ecosystem in particular really helping uncover that AI jobs are not just at OpenAI or Google or Microsoft. In fact that there's a lot of other sectors, companies, geographies where there are, there are opportunities in AI. You know, some of the most interesting applications of computer vision are not, are not happening in the tech sector.

Right.

You know, I think, you know, there's some really interesting applications of, of not only sort of broad based AI technologies, but actually really niche applications that have been applied to industrial use cases with digital twins that we've seen some really interesting things happening not only as we look geographically and on a sector basis, but also as we look with companies entering new business lines that they might not have been able to tap into and exploiting the efficiencies of AI to do that.

Yeah, sorry, yeah, just very quick, is that what universities do? Of course, is to train people for employment? I mean that's of course to be good citizens. But from an employment point of view that now training people for AI is look around at the companies and industries in the same geography. It's in Kansas, it's in Minnesota, it's not just California or It's not just D.C. or New York. So that's the kind of the big takeaway.

All right, and then I think we'll do two more questions here. So, Juan, asking how can the workforce love this question? How can the workforce better prepare themselves to qualify for all of these AI jobs that are, that are up here on the tool. Great. Probably what everyone's thinking, Evan, what are your takes. Takes on that?

Yeah, I mean, I think, you know, first thing I'll, I'll put in a plug for you, Jordan. You know, if you listen to everyday AI, you probably are better informed about the industry and understanding what's going on, industry trends. I think there's also just an understanding of what skills are relevant today. Right. I found myself on a call yesterday with a college student friend's son. And when he said he doesn't code, I thought to myself, five years ago that would have been a profound problem in the job market. Now it's not necessarily so. Learning to use tools that help you actually upskill is the primary way you're going to get a job. And so that ended up being the crux of our conversation was, hey, how do you use these AI tools to fill skills gaps without having to spend 3, 4 years learning how to do those things?

Speaking of skills, we'll leave this as the last audience question, so maybe Anil, you could take this one. So Douglas asking, is there a way to filter by skills as an example, Python data visualization, large language models, et cetera. Is there a way to do that right now?

Not right now, but that's exactly one of the kind of immediate project for us is we have already started looking at within AI jobs, what are the kinds of AI skills and which skills are growing in demand, which are declining in demand, what's the distribution across geographies, across industry sectors. But certainly of course, Python data visualization, LLMs, those are core, those are hardcore.

All right, we've talked about a lot here maybe as we wrap up this show, maybe just one more question for each of you. Evan, now that you've had this out for an extended period of time and when you're putting in and refreshing this data monthly, what are the things that you yourself are looking for as probably one of the few people that uses this the most, having helped build it. But what are the things that you're looking for in terms of new data in the future to compare where we're at now and what should that tell us about where kind of the, the future economy in the US is heading?

Yeah, I mean this is always an interesting question when you look at trend in any data set is we've been starting from a really low base, right? There weren't that many AI jobs over the last few years and as that number grows, we would expect the growth rate to decline. I suspect that's not going to happen for a while. And that's a macro story, right, that we expect actually that there's probably going to be an acceleration in the number of AI jobs and then it's going to level off a bit. And so I think that's what I'm looking to see and then looking to see that broken down by sector and by geography to really understand where those AI jobs are gravitating to who are the leaders versus the laggards, right? We know the big tech companies are always going to be the leaders in developing and adopting new technologies. The question becomes, what about that next wave when we start seeing that every financial institution, every healthcare institution, right? We haven't talked much about healthcare today, but healthcare has a huge amount of data in it and a Big opportunity for AI, but we haven't seen widespread implementation there, in part because of HIPAA questions and data security questions. I think as those questions start to get answered and solutions are built, we're going to see massive growth in AI jobs across sectors like healthcare.

Yeah, that's a great point. Yeah. Just industries are adapting this at different rates. And then, Anil, for you, as we wrap up today's show, how would you suggest that the average person, you said that this is a public resource, which I definitely agree it is. But Anil, how would you recommend, you know, the average person, maybe they're, you know, either looking at a future job, they're interested in AI. You know, how would you suggest someone to use this tool to best kind of understand where the AI job market and this dispersion is going?

Right, right, right. Yeah. So, I mean, you know, I look at the analogy of going out fishing. So obviously you want to catch a fish, you want to get a job, but you also want to figure out, what pond do I go fishing. You can't apply for a job through our data portal. You got to go to company career pages. But what our data portal can do is already able to do is to give people information about where are the different ponds. If you are sitting in Kansas and you want to look at what kind of pond do I have in Kansas or neighboring states, you can go to this data portal. Or you may say, you know, hey, I want to move to California or I want to move to Austin or to New York or Washington, D.C. but maybe not so. So I think that's the way in which people today can use this data.

That's, that's such. I love that analogy, Anil, like, go find the pond in your backyard. Well, you all have created such an amazing resource that I think is going to really benefit the public both now and moving forward. So, Anil and Evan, thank you both so much for joining the Everyday AI Show. We really appreciate your time.

Jordan, thank you very much.

Thank you so much for having us.

All right, and hey, as a reminder, everyone, we covered a lot on today's show talking about the dispersion of AI jobs and why it matters. There's always more, so make sure you check out our website. Go to your everydayai.com Sign up for that free daily newsletter. We'll be recapping, sharing more resources as well. If this was helpful, please consider sharing this with your network. Tell someone about it. This is such a great resource. Please let others know and also join us tomorrow. We're going to be going over quad 3 and if infrapics new model is actually better than chatgpt and gemini. So thank you for tuning in. We hope to see you back tomorrow and every day for more Everyday AI. Thanks y'. All.

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