Everyday AI Podcast – An AI and ChatGPT Podcast - EP 222: The Dispersion of AI Jobs Across the U.S. - Why it matters

Episode Date: March 6, 2024

Awesome Stuff From Our Partner, NVIDIA -Register for the FREE virtual NVIDIA GTC Conference or buy tickets to the in-person event and fill out this form here: https://www.youreverydayai.com/nvidia-giveaway/When we talk about AI jobs, we normally think that AI is just taking our jobs. But that's not necessarily how it's happening in the real world right now. AI jobs are being dispersed all across the US. Anil K. Gupta, Michael Dingman Chair in Strategy, Smith School of Business, The University of Maryland, and Evan Schnidman, Co-Founder & CEO of Outrigger Group, join us to take a look at AI jobs in the US.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode pageJoin the discussion: Ask Jordan, Anil, and Evan questions on AI jobsRelated Episodes:Ep 132: Enterprise AI – Future Careers and How to PrepareEp 106: Using AI to Land a New CareerUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:01:40 Daily AI news04:45 Intro to Anil and Evan and the 06:30 UMD LinkUp AI Map11:16 Interactive map shows dispersion of AI jobs.15:35 Key sectors for AI talent: finance, retail.20:07 AI job growth surprises, emphasizes Washington DC.23:16 Anil discusses AI's impact on future jobs.27:18 Upcoming research will cover sectors, skills, geographies.28:41 Universities can use data to uncover AI opportunities.32:52 AI job growth expected to accelerate, then plateau.35:02 Access company career pages for information. Use data portal to find geographical information.Topics Covered in This Episode:1. The UMD LinkUp AI Map Project 2. AI Workforce and Labor Market3. Understanding AI Job Growth in Various Sectors4. Dissecting the Job Landscape and the Rise of AI Jobs5. Future of AI JobsKeywords:AI Jobs, AI technology, Artificial Intelligence, Job market, Labor market data, LinkUp, Washington DC, AI job growth, Defense sector, Intelligence agencies, Technology companies, Banking, Insurance, Retail, Consulting firms, Deloitte, Accenture, John Deere, Caterpillar, Heavy equipment industry, UMD link up AI maps tool, Geographic dispersion, IT Jobs, Total employment, AI skills, Silicon Valley, Outrigger Group, UND Link Up AI Maps project, AI workforce, AI job opportunities.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. 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 disparate.

first 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 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 to 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.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 in sales, AI and entrepreneurship. We've probably already talked to five or ten 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 Open AI and Microsoft.

And Microsoft finally fired back and filed a motion to dismiss the case. So Microsoft is challenging the New York Times lawsuit in 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 open AIs chat GPT on the news business. All right. Speaking of Open AI, a lot of Open AI news today. So Open AI.

also first publicly responded to Elon Musk lawsuit against the company. We just talked about this yesterday on the Everyday AI show, but Open AI 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 criticized in the company for being, quote, super closed source. All right. So a couple things to know here. Elon and Open AI were at odds over the company's structure and the decision to become a for-profit entity. And originally Elon had supported raising hundreds of millions of dollars for the company, but also later proposed merging Open AI with Tesla so the two of them could combine, which is now kind of what they're doing, but with Microsoft. And Open AI has obviously faced criticism and investigations over its own pretty complicated 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 GPD 4? All right. So Anthropic released Cloud 3 earlier this week, its newest model. But an AI researcher from Berkeley recently noted that the test that Anthropic shared appeared to compared GPD4's original public model with Claude and not its updated and more powerful GPT4 turbo.

So while Anthropics 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 U.S. 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 this show. There we go.

And let me do a quick introduction. We have Anil Gupta, the Michael Dingman chair and strategy at the business school at the University of Maryland and Evan Schnidman, the co-founder and CEO of Outrigger Group. Both of you 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, George.

And 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 in 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 a 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 payroll. And 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 maybe, Evan, could you walk us through a little bit? How did this project come to be? Maybe give us a little background on how you and a Neal 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.

So, Evan, walk us through kind of the creation of this project. Yeah, absolutely. So, you know, Neil and I had a 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 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 investing 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. The easiest way to tell whether a company was actually investing in AI 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 a Neil and I and building this out. And so, you know, LinkUp was gracious enough to supply the underlying job market data here and really allowed us 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? 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, you know, 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, you know, 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, you know, one thing that I like, and maybe let's just get to the point of, you know,

the dispersion of AI jobs. But one thing I really like about this map is it's interactive, right? So you can, you know, hover, 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, et cetera. 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, 2,2004 AI jobs,

some of your main findings are showing it's not just California where all of these AI jobs are popping up. 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. one in a 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 is 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, I think neither Anil nor I had any expectation that that was the case. And Anil, I guess I'll I'll let you speak to this because you actually live in that area. Yeah, right, right, right. And, you know, I've been living here for 40 years, but I did not expect. But part of the reason, you know, 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 hut from a job's 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, you know, like 2017, you know, back in China, there was the event Google Deep Mines Alpha Go beat Kherjee 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, you know, 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, you know, 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, you know, so this is kind of, and it's all a recent story.

And, you know, 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 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. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI assistant. now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the assistant.

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So in terms of sectors, the big sectors, of course, is 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, you know, for example, I mean, Walmart, Target, they're investing a huge chunk of capital, if you will, 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 Pentonville. And so, 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 Exensure and Boozell 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. And I add to that, 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, you know, so just to point to specific companies here, if you look at the difference between John Deere and Caterpillar, right, John Deere has rapidly staffed up on AI personnel versus Caterpillar Hazard. And that's just taking different tax within the same industrial sector there, right? The heavy equipment space.

And one company really focused in on that and another one didn't. And so we're seeing some of that bifurcation within the sector as well. And I think that's, as I 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 a 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, who are curious about, you know, how AI, jobs are being implemented, you know, across different industries, across different sectors, geographically, et cetera. 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 map, 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 have 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 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 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 a couple of pieces here. So first and foremost is probably to remind everybody, this is free, right? We really built this initially as a service to the community. And so our view has always been, can we add knowledge to the community? And so we initially just launched a white paper just to explain what we've done here,

to explain the geographic findings. And 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. 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 the D.C. area not only is out punching its weight, but out punching all of those except for the Bay Area. Right. It has been the key story that I think folks have really pointed to.

I also think that the other big story is the question of, you know, we've, obviously, the labor market has taken a turn here for the vast majority of tech jobs over the course 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 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 rules. including AI, now we're seeing a dispersion, right? We're seeing that that widening gap between AI jobs and IT jobs.

Yeah. And just to build on what Evan was saying is that, you know, December 2020 or late November 2022, as we know, was quote unquote, the AI shock. That's the launch of Jack GPT and then GPD4 and so on. And so we compared December 22 was the low point in terms of the number of the AI jobs. because, you know, they had been companies at Gorse themselves, they had overstaffed and so on. But then compared to December 2022 until 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 curve. 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, you know, I mean, day in and day out, you read, you know, 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, you know. So you see this kind of a widening gulf. So, you know, of course, Andrewson, Mark Anderson, A16C, software eats the world. One could add on top of that maybe AI eat software. 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 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 digital 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 co-pilots, 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, you know,

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 US. 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 of course, 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 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-text 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 as 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. Evan, Evan, that's a great point.

And data obviously is at the heart of everything, not just for 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 a lot of questions. Maybe we can do a quick little rapid fire, so to speak. So we don't accidentally keep you all for two.

hours. But a great question here from Monica, how do you see the UMD link up 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 types of AI skills, you know, like computer vision skills, language skills, other types of skills. So in terms of the what types of skills in what sectors and what geographies? Another thing is that we 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 US, 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. Right. Yeah. I think, Anil, I think when people use this, and it is just at AIMAPs.A.I. 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 opportunities in AI. You know, some of the most interesting applications of computer vision. are not happening in the tech sector, right? I think there are some really interesting applications of not only 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. Just a very quick is that, you know, 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, you know, training people for AI is look around at the companies and industries in the same geography. You know, it's in Kansas.

It's in Minnesota. You know, 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 one 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 up here on the tool?

Great. Probably what everyone's thinking. Evan, what are your takes on that? Yeah. I mean, I think, you know, first thing, I'll open. in a plug for you, Jordan. 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. 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 three, four years learning how to do those things? And, hey, 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, I mean, Python, data visualization, L&Ms, I mean, those are core.

Those are hardcore. All right. So we've talked about a lot here. So maybe as we wrap up this show, maybe just one more question for each of you. You know, Evan, so now that you've had, you know, this out for an extended period of time. And, you know, 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 future economy in the U.S. is heading? Yeah, I mean, this is always an interesting question. You look at trend in any dataset 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 growing.

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 health care institution, right? We haven't talked much about health care today, but health care 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 across sectors like healthcare. And yeah, that's a great point.

Yeah, just, you know, industries are adapting this at different rates. And then, Anil for you as we wrap up today's show, how would you suggest, right, that the average person, you know, you said that this is, you know, like a public resource, which I definitely agree it is. But Anil, how would you recommend the average person, maybe they're either looking at a future job, they're interested in AI? 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? And so you can't apply for a job through our data portal. You've 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 points, you know. And 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 I think that's the way in which people today can use this data. 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 AIA. 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 everyday AI.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 us. others know, and also join us tomorrow. We're going to be going over Claude 3 and if Infraopic's 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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