Ep 222: The Dispersion of AI Jobs Across the U.S. – Why it matters

AI-Transformation: US Industries Investing in Advanced Technology

Identifying the growth and dispersion of artificial intelligence (AI) jobs across the United States provides insights into how industries are adopting AI technologies. While traditionally we might associate AI with the tech or IT sectors, the true story speaks to the expanding applications of AI across various industries spanning defense, finance, healthcare, and retail.

Validation From Labor Market Data

Labor market data, particularly from sources like LinkUp, provide empirical evidence of the dispersion of AI jobs. Surprisingly, Washington DC, often associated with policy and government, has emerged as the second-largest AI hub in the U.S. This can largely be attributed to a surge in need for sophisticated AI applications in defense and intelligence agencies.

The Emergence of AI Hubs Across the US

The common misconception that AI is the exclusive territory of tech hubs like Silicon Valley has been disproven by the rise of new AI hubs. Whether it's AI applications in banking and insurance in New York, or medicinal and biotech AI in Boston, there's surge in demand for AI talent across the country.

Real-world AI Applications: Observations from Heavy Equipment Sector

Spotlighting practical AI applications, businesses such as John Deere and Caterpillar in the heavy equipment space have adopted AI for processes and product design. These examples exhibit AI's transformative potential when fully integrated into industrial and traditional operational spaces.

Tool for Navigating AI Job Landscape: UMD Link Up AI Map

Navigating this volatile and expanding job market for AI is aided by tools such as the UMD Link Up AI Maps. Such resources offer data insights delineating the distribution of AI jobs across industry sectors. As the first tool of its kind, it's received considerable attention for unveiling the geographic dispersion of AI jobs.

The Future of AI Jobs

While the acceleration of AI job creation is to be expected, it's crucial to prepare for a future landscape where the majority of jobs may indeed require AI skills. To track this transformation, it's beneficial to audit total employment focusing not just on AI-dedicated jobs but also peripheral roles demanding AI skills.

Corporate Investment in AI: A Reality Check

Navigating the discourse around AI, it's crucial to recognize the difference between actual commitment to AI technology and mere buzzwords for marketing purposes. Recognizing this distinction is vital in understanding which industries and companies are genuinely investing in AI.

AI Talent Acquisition Strategy

A trend worth mentioning, consulting firms such as Deloitte and Accenture are ramping up their AI talent acquisition. This is testimony to the anticipated demand for AI-empowered solutions that these consulting giants naturally anticipate from their client base.

AI job growth in the US signifies not just a consolidation of AI focus in tech companies but indicates a transformative shift across various non-tech industries as well. Recognizing this shift early can provide a definitive edge to businesses and potential AI professionals alike. Today, AI is not just on the periphery; it has taken center stage in catalyzing future growth across sectors.

Topics Covered in This Episode

1. The UMD LinkUp AI Map Project
2. AI Workforce and Labor Market
3. Understanding AI Job Growth in Various Sectors
4. Dissecting the Job Landscape and the Rise of AI Jobs
5. Future of AI Jobs

Podcast Transcript

Jordan Wilson [00:00:16]:
When we talk about AI jobs, normally, we just think of AI taking our jobs. It's a 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 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 a data geek and someone that follows, AI and and the job market all the time, we're gonna be talking about the dispersion of AI jobs across the United States and why it matters.

Jordan Wilson [00:00:58]:
Alright. 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 everydayai.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.

Jordan Wilson [00:01:24]:
And I tell people our website is a free generative AI university. Now more than 210 past episodes, no matter what you wanna learn, whether it's AI in sales, AI in entrepreneurship, we've probably already talked to 5 or 10 experts. So you can go learn there and go experts. So you can go learn there and go read all of our old newsletters as well. Alright. So let's start the show as we do every time with going over the AI news. Alright. 1st, Microsoft is challenging the New York Times lawsuit in its first public response to the December lawsuit from the New York Times.

Jordan Wilson [00:01:56]:
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 I I love this. They compared it to the VCR, from the eighties. And essentially, you know, so Microsoft compared the New York Times lawsuit, to Hollywood's resistance to the VCR, which was created in the seventies 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 chat gpt on the news business. Alright. Speaking of OpenAI, a lot of OpenAI news today. So OpenAI also first publicly responded to Elon Musk's lawsuit against the company.

Jordan Wilson [00:02:45]:
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. Alright. So, couple things to know here. Elon and OpenAI were at odds over the company's structure and the decision to become a for profit entity. And, originally, Elon had supported, raising 100 of 1,000,000 of dollars for the company, but also later proposed merging OpenAI with Tesla so the 2 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. Alright.

Jordan Wilson [00:03:45]:
And last but not least, does Claude 3 really beat g p t 4? Alright. 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 GPT four's original public model with Claude and not its updated and more powerful GPT 4 turbo. So while Infropic'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 GPT 4 turbo model still reigns supreme in areas where they're able to benchmark it. Alright. Also, we'll be diving more into quad 3 tomorrow and doing some real time comparisons. So make sure to join us. Alright.

Jordan Wilson [00:04:33]:
That's enough for the AI news. As always, you can get more at our website. But, today and right now, that's not worth talking about. We're talking about the dispersion of AI jobs across the US and why it matters. Alright. So it's not just me today. I actually have 2 experts. So we have a treat for y'all, interviewing 2 people today.

Jordan Wilson [00:04:51]:
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 in strategy at the business school at the University of Maryland, and Evan Schmidman, the cofounder 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?

Anil K. Gupta [00:05:12]:
Yeah. So, you know, as 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.

Jordan Wilson [00:05:29]:
Alright. And then, Evan, thank you as well for joining us. Can you tell us a little bit what you do as the cofounder and CEO of Outrigger Group?

Evan Schnidman [00:05:37]:
Yeah. Absolutely. And thanks for having us here, Jordan. You know, I I 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 and really build the next generation of of data and AI companies.

Jordan Wilson [00:06:06]:
Perfect. Alright. So let's let's go to the big picture here. So, we've shared this in our newsletter before, but for our livestream audience, maybe you haven't seen this. So I'm gonna throw this on the screen for our podcast audience. We're gonna try to do our best to go ahead and describe, what what we have here going on. But, maybe, Anil, if you could walk us through. So, you guys just recently, released this new, UND link up AI maps.

Jordan Wilson [00:06:31]:
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.

Anil K. Gupta [00:06:45]:
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. 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. Not just 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.

Anil K. Gupta [00:07:39]:
And we create monthly updates. We create white papers. We create ranking sheets. And, again, everything is there's nothing behind a paywall.

Jordan Wilson [00:07:49]:
And and I I love that, you know, because this is, I think, a 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, gonna be new jobs that are in in maybe new sectors that didn't, exist before? But but maybe, Evan, could you walk us through a little bit? How did how did this project come to be? Maybe give us a little background on on how you, and Anil met and kind of what was the reason for for building, this project that we're looking at now. And, hey, if if you are curious, we'll have this in the newsletter, but it's aimapsdotai. So, Evan, walk us through kind of the the, creation of this project.

Evan Schnidman [00:08:35]:
Yeah. Absolutely. So, you know, 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 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 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 continue 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 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 I'm very close to the the head of strategy there, John Norberg, who I I think has been integral in this this project. John's been an amazing partner for for both Anil and I in building this out. And so, you know, LinkUp was was gracious enough to supply the the underlying, job market data here, and and really allowed us to to tap into not only what's going on geographically, but broken down by sector and each individual company.

Jordan Wilson [00:10:14]:
And 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 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, you know, plots that are up here on the graph. What is actually covered as an AI job?

Anil K. Gupta [00:10:36]:
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, the economy is is 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.

Jordan Wilson [00:11:16]:
And and, you 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 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, etcetera. 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, 2,200 and, 34, 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,

Evan Schnidman [00:12:14]:
go ahead. Yeah. Yeah. Yeah. Happy to jump in on this. So I think so, Jordan, you highlighted that California is number 1 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 DC area.

Evan Schnidman [00:12:37]:
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 that was the case. And and, Anil, I guess I'll I'll let you speak to this because you actually live in that area.

Anil K. Gupta [00:12:52]:
Yeah. Right. Right. Right. And and, you know, I've I've been living here for 40 years, but I did not expect. And and but part of the reason, you know, then I was saying, why do why did I not expect? And so what happened is that as our data tells us that the rise of the DC region as the 2nd 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 and what happened really, you know, like, 2017, you know, back in China, there was the event, Google DeepMind's Alpha Go beat Kejie at the, game of Go, and that was the Sputnik moment for China.

Anil K. Gupta [00:13:37]:
Within 2 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 adviser 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, 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.

Anil K. Gupta [00:14:32]:
There is an exception, of course. For example, you know, we got Capital 1. So Capital 1 is a power in terms of it's not the biggest bank, but it has more AI jobs than even JPMorgan. So you got that. You got, Amazon's HQ 2. So so there are the you know? So this is kind of and it's all a recent story.

Jordan Wilson [00:14:57]:
And and, you know, a great a great question here and very, very timely, from from from Liz. So Liz is asking, are there any specific industries or sectors where AI growth is expected to be particularly significant? So, you 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 are going and maybe something that maybe caught one of you 2 off guard when it came to sectors.

Anil K. Gupta [00:15:35]:
So in terms of sectors, the the 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 2, which are the very data intensive sectors. And so, naturally, finance would jump up. Correct? You know, 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 and and 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.

Anil K. Gupta [00:16:19]:
And the entire digital efforts, pretty much the entire digital efforts at Walmart, are actually based in Silicon Valley, not in Bentonville. And so so so and and then what's happening is at that as AI, becomes diffused across different sectors, that not every company or or even every large company is necessarily hiring their own AI people. So the sync from a sector point of view, the single biggest growth we found is in companies like Deloitte and Accenture and Boozell and Hamilton and and 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.

Evan Schnidman [00:17:09]:
And and I 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 on a sector by sector basis is the bifurcation within some sectors. Right? There are certain sectors that where, you know so just 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 has it. Right? And that's they've just taken different tax within the same industrial sector there.

Evan Schnidman [00:17:43]:
Right? They're they're the the 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 Anil alluded to, the more data intensive sectors, tend to be, you know, everybody is involved in in implementing AI in one way or another. Whereas the 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.

Jordan Wilson [00:18:18]:
Yeah. Yeah. That's interesting. So 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, etcetera. 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?

Anil K. Gupta [00:18:55]:
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 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.

Jordan Wilson [00:19:32]:
Yeah. And and I guess something that's even in important, you know, maybe we should have talked about this at the top of the show. This this, tool that you that you guys have 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 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?

Evan Schnidman [00:20:07]:
Yeah. So so I think, you know, couple of couple of pieces here. So first and foremost is probably to to remind everybody this is free. Right? We we really built this as an initially as an as a service to the community. And so, you know, our our view has always been, you know, can we add knowledge to the community? And so what are we we initially just launched a white paper just just to explain what we've done here, right, explain the geographic findings and what I think we've been pleasantly surprised by the fact that 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 DC component. Yeah, I think everybody expects to see AI jobs popping up in in the Bay Area, in Austin, Texas, in New York, in Boston. But, you know, I I think to see that that, you know, that that the DC area not only is out punching its weight, but out punching all of those except for the Bay Area, right, has been the the key story that I that I think folks have have really pointed to. I also think that the the the other big story is is the question of you know, 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.

Evan Schnidman [00:21:29]:
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 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. Right? We're seeing we're seeing that that that widening, gap between AI jobs and IT jobs.

Anil K. Gupta [00:21:56]:
Yeah. And and just to to to build on what Evan was saying is that, you know, December 2022 or late November 2022, as we know, was, quote unquote, the AI shock. That's the launch of chat GPT and then GPT 4 and so on. So and so we compared December 22 was the low point, in terms of the number of AI jobs. Because, you know, there had been companies at course 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 curve.

Anil K. Gupta [00:22:41]:
Of course, the total number of IT jobs is gonna 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, one you know, of course, Anderson, Mark Anderson, a 16 c software eats the world. One could add on top of that, maybe AI eats software.

Jordan Wilson [00:23:16]:
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, are you, are we maybe seeing a shift in, in, right, and maybe this is later down the road going back to the, the 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 gonna be leveraging, you know, prompting and, you know, maybe Microsoft copilots. Right? Are we gonna see that soon? And and 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?

Anil K. Gupta [00:24:16]:
Yeah. So, I mean, the our data, you know, I mean, the the the the the 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 gonna 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 the the sort of a value chain.

Anil K. Gupta [00:25:01]:
So you've got science, then you've got technology, then you've got applications of that technology into products, 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 gonna become an AI job. But then the other part of the story is that do companies like John Deere, companies like, Capital 1, companies like Walmart, They're going to need even as every job becomes an AI job. Do they need technical AI skills?

Evan Schnidman [00:25:48]:
And I'll add to that. There 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 tens 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.

Evan Schnidman [00:26:28]:
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 from each individual use case, that becomes a data question as much as it's an AI question.

Anil K. Gupta [00:26:48]:

Jordan Wilson [00:26:48]:
Evan Evan, that's a great point. And, you know, data obviously heart of everything, not just for, you know, the, I guess the future of 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 we have a lot of questions. Maybe we can do a a quick little, rapid fire, so to speak, we don't, accidentally keep you all for 2 hours. But but a great question here from Monica. How do you see the UMD link up AI maps project evolving?

Anil K. Gupta [00:27:18]:
Yeah. So, I mean, the 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 1 is look at different sectors. The because this map, currently, we looked at geographies. So we look at sectors. 2nd, 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 the 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 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.

Anil K. Gupta [00:28:09]:
We have data on China, but it's not as comprehensive, as we have data on many other big economies.

Jordan Wilson [00:28:17]:
Right. Yeah. It's I think, 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 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?

Evan Schnidman [00:28:41]:
Yeah. I mean, I I think there's a lot of ways for universities to not only leverage this data, but but a lot of of other tools in the ecosystem. In particular, really helping uncover that 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? You know, I think you know, there are some really interesting applications 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 that we've seen some really interesting things happening, not only not only as we look geographically and and on a sector basis, but also as we 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.

Anil K. Gupta [00:29:45]:
Yeah. And and it sorry. Yeah. Just just very quick is that, you know, what universities do, of course, is to train people, for employment. I mean, that's of course, should be good citizens. But from an employment point of view, that now, you know, training people on, for AI is look around at the companies and industries in the same geography. You know? It's in Kansas. It's in Minnesota.

Anil K. Gupta [00:30:10]:
You know? It's not just California or it's not just DC or New York. So that's, the the kind of the big takeaway.

Jordan Wilson [00:30:18]:
Alright. And then I think I think we'll do 2 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 that are up here on the tool? Great. Probably what everyone's thinking. Evan, what are your, takes takes on that?

Evan Schnidman [00:30:34]:
Yeah. I mean, I think, you know, for first thing, I'll, I'll I'll I'll I'll open in a plug for you, Jordan. You know, if you you listen to EverydayAI, you you probably are are better informed about the industry and understanding what's going on in industry trends. I think there's also just an understanding of what skills are relevant today, Right? You know, I I found myself on a call yesterday with a, a college student, friend's son, and, you know, when he said he doesn't code, I thought to myself, you know, 5 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, you know, 3, 4 years learning how to do those things?

Jordan Wilson [00:31:31]:
And and, hey, speaking of skills, we'll 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, etcetera. Is there a way to do that right now?

Anil K. Gupta [00:31:47]:
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, and which skills are growing in demand, which are declining in demand, what's the distribution across geographies, across industry sectors? But, but certainly, of course, I mean, Python data visualization, L and M's, I mean, those are core. Those are hardcore.

Jordan Wilson [00:32:16]:
Alright. And so so we've talked about a lot here. So maybe, as we wrap up the show, maybe just one more question for for each of you. You know, Evan, so now that you've had, you know, this 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, you know, 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

Anil K. Gupta [00:32:51]:

Evan Schnidman [00:32:52]:
Yeah. I mean, this is always an interesting question when 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 gonna happen for a while. And that's that that's a macro story, right, that we expect actually that that that there's probably gonna be an acceleration in the number of AI jobs, and then it's gonna gonna level off a bit. And so I 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 where those AI jobs are gravitating to, who are the leaders versus the laggards. Right? We know the the big tech companies are always gonna be the leaders in in developing and adopting new technologies.

Evan Schnidman [00:33:41]:
The question becomes, what about that next wave when we start seeing that 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 of HIPAA questions and and data security questions. I I think as those questions start to get answered and solutions are built, we're gonna see massive growth in in AI jobs across across sectors like health care.

Anil K. Gupta [00:34:11]:
And and yeah.

Jordan Wilson [00:34:12]:
That's a that that's a great point. Yeah. Just, you know, industries are adapting this at at different rates. And then, Anil, for you as as we wrap up today's show, how would you suggest, right, that the average person you know, you you said that this is, you know, like a a public resource, which I definitely, agree it is. But, Anil, how would you recommend, you you know, the average person, maybe they're, you know, either looking at a a future job, they're interested in AI. You 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.

Anil K. Gupta [00:34:46]:
Right. Right. Yeah. So, I mean, you know, I I I look at the analogy of going out fishing. So, obviously, you want to catch a fish. You wanna get a chop. But you also wanna figure out what pond do I go fishing. And so you can't apply for a job through our data portal.

Anil K. Gupta [00:35:02]:
You gotta 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. 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 wanna move to California, and I wanna move to Austin or or to New York or Washington DC, but maybe not. So so I I think that's the the the the the way in which people today can use this, data.

Jordan Wilson [00:35:39]:
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 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.

Anil K. Gupta [00:35:59]:
Jordan, thank you very much.

Evan Schnidman [00:36:00]:
Thank you so much for having us.

Jordan Wilson [00:36:02]:
Alright. 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 dotcom. 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.

Jordan Wilson [00:36:24]:
This is such a great resource. Please let others know, and also join us tomorrow. We're gonna be going over Claude 3 and if Anthropic's new model is actually better than chat GPT 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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