Navigating the AI Talent Landscape: How to Capitalize in Hypercompetitive Markets

[0:00:20] John Lynch: Hi, everyone. Thank you for joining us today for our new webinar on navigating the AI talent landscape. If you're a customer or if you're familiar with TalentNeuron, you may have noticed something new about our webinar today. Today marks the soft launch of our new TalentNeuron brand, which will be rolling out in full over the next couple of months. I hope you like it. It's a little new, it's forward-facing, and I hope it strikes a chord with you, just like our topic today. In today's webinar, we'll provide data on how organizations recruit and search for AI talent and where that competition is playing out.

TalentNeuron is uniquely positioned to provide data and specific skills, and we help our customers find opportunities that give them a competitive advantage.

If you're interested in finding out how TalentNeuron can help your organization at any time during today's webinar, please hit that "Request a Demo" button at the top of your screen. We'll be happy to continue the conversation in any form. Today, we'll review a lot of data and take some time to answer questions at the end. I want to make sure that you can orient yourself here. I'll get on to that in a moment. If you drop any questions, we'll get to those at the end of the session. I hope you enjoy everything we have to share with you today.

Just a little orientation here on the presentation screen. This is the dashboard you'll be seeing in front of you. Looking at the right-hand column here, you'll see a Q&A function. That's where you can drop in your questions, and like I said, we'll get to them at the end of the session. If we don't, that's fine — we'll follow up afterward. There's also a media tab there, and that's a great place for finding associated content on recruiting, AI talent, and many of the things we do here at TalentNeuron.

If you would like to expand your screen, you may need to do so because there are a few quite detailed graphics here. There's a box in the bottom right-hand corner of that screen. If you click on that, it will blow up your view, and you'll be able to see everything in detail.

So, my name is John Lynch. It's a pleasure to have you here. I'd like to introduce our panel for today. My colleagues Lynne and Parker draw from our strategic consulting and advisory teams. Lynne and Parker, thank you so much for being here. It's a different time for all of us — early in the morning. Lynne, would you like to kick us off?

‍

[0:02:58] Lynne Mayers: Yeah. Thank you so much, John. Hi, I'm Lynne Mayers, a Senior Consultant with TalentNeuron's strategic consulting team. I've been with TalentNeuron for the past seven years, advising our clients on using external talent intelligence to drive their talent and location strategies. Previously, I was advising senior HR leaders for 16 years before that. I'm excited for the conversation today. Parker?

‍

[0:03:24] Parker Devlin: Thank you, Lynne, and hello, everyone. My name is Parker Devlin. I'm a Senior Advisor with TalentNeuron's client engagement team. I've been with TalentNeuron for over three years, advising clients on leveraging our solution. I am also delighted to be here for this conversation today.

‍

[0:03:44] John Lynch: Awesome. Thank you both. So, let's get started with a bit of background. Lynne, would you mind setting the scene a bit? We're trying to understand why organizations are looking to AI as a capsule solution for many business challenges today. In summary, what are they trying to solve?

‍

[0:04:11] Lynne Mayers: Yeah. I think the thing is that AI can be used in so many ways within an organization — to innovate the products and services it offers, to transform how it sells to and services its customers, and to increase internal efficiencies. And in the top left, we see that it's already making knowledge workers more productive. Then, on the bottom left, 78% of business leaders say they see AI as a source of competitive advantage. So, it is true that organizations have great hopes for AI. But there is a need to prioritize and focus on where ROI will be greatest and deliver that competitive advantage.

And AI has really been around for years in various forms and applications — robotics, computer vision, natural language processing. But with the release of ChatGPT and other large language models last year, things really kicked into high gear because of the potential for generative AI to accelerate these various applications. While some organizations have defined their roadmap for how they plan to harness AI, others are still working out their strategy. We're seeing that, in many cases, a lot of experimentation is happening in various pockets of the organization — even in HR itself. Experimentation is great, and it's necessary, but it's still important for organizations to have a really clear vision of how they intend to leverage AI to compete.

Finding AI talent with the right skills is challenging due to the specialized and scarce skill requirements. In fact, on that bottom right there, about half of business leaders say that skills availability is a barrier to adoption for their organizations. Parker, I want to bring you in here because you often work with our clients and are looking to extract data insights from our platform. What are some of the themes you hear from them in those conversations?

‍

[0:06:23] Parker Devlin: Yeah. Thanks for the question, Lynne. First, to echo your point, clients seek to understand how their peers are applying generative AI in their organizations. They're seeking insight from our competitor intelligence on what types of roles and skills their competitors are hiring for in this domain. This suggests that these organizations are still in the phase of defining their generative AI vision and roadmap. Some organizations have already defined their vision and are looking for AI talent. They want to understand which markets are most favorable for talent, both nationally and globally. And if they invest in those locations, what should they expect in terms of competition?

‍

[0:07:17] John Lynch: Awesome. I think this is a good place to introduce some of our data. We found in the TalentNeuron platform that the story about the demand increase is compelling. If we look at demand increases over the past two years, there's been an overall increase. This is a pretty strong and sustained increase. This isn't just a spike.

This analyzes the number of AI engineering roles advertised worldwide since January 2023. There have been some ups and downs, but this is really a continued and sustained demand growth story. Would you like to talk more about what that information tells us, Lynne?

‍

[0:08:02] Lynne Mayers: Sure. As you said, John, we've seen a steady increase in global demand for AI engineering talent since January of 2023 — with a couple of blips but a steady upward march overall. That's attributable to the increasing rate of AI adoption and the race to develop cutting-edge technologies as businesses recognize AI's potential to drive business value.

It's really interesting because this was against the backdrop of a more muted year for tech hiring in 2023. I looked at our data, and we saw that software engineers are hiring down about 24% globally compared to 2022. So, we looked at what percentage of software engineering talent demand was attributable to AI engineer-specific hiring, and we see that share increasing over time. That shows us that the share of investment organizations make in AI talent, as a percentage of overall technology hiring, is increasing.

We'll talk more about this next, but breaking this down and looking at trends by country is so interesting. We don't see demand increasing in the same way everywhere. It's increasing in some countries, but it's actually neutral or even declining in others. We're going to talk about that next.

‍

[0:09:28] John Lynch: Awesome. I think that's the really compelling takeaway. When we identify specific places where there's a gap between demand and supply of talent, there are a lot of great takeaways and opportunities for our audience today.

To orient us here, we're going to explore this data in a bit more detail. This is a deeper analysis of the top 20 global markets for AI engineering talent, ranked by the availability of talent and skills. These are color-coded by region. You'll see Guangzhou (China), Paris (Western Europe), etc.

If we look at the demand for AI engineering roles in these markets, it's pretty interesting. But this is only part of the view. We have this demand view, which shows that in established tech centers like the Bay Area, Bangalore, Singapore, Beijing, and New York City, there's strong demand for what we'd call a sophisticated set of skills. This is the expected view.

But if we flip this into a different view of demand growth over the past few years, we see something interesting. Total demand gives us an established view, but demand growth shows that the strongest growth comes from less established cities. If we run through Pune, Hyderabad, Chennai, and Mexico City, these are where we're seeing stronger growth in demand. Bangalore and Singapore are somewhat expected, but we'll dig into that in more detail later.

There's also a compelling negative growth story in Shenzhen, the Bay Area, New York City, Shanghai, and Beijing — places where you'd expect to see this overall increased demand globally play out. This is actually where we're seeing a negative trend.

There's a lot to parse out here and many different ways we could break this data down. Lynne, could you tell us more about what you think this story is telling us?

‍
[0:12:03] Lynne Mayers: Yeah. And I think the bigger context here is that 2021–2022 were boom years for technology hiring. Organizations were flushed with cash. They were in this frenzied race to accelerate their digital ambitions, and they really hired wherever they could find the talent at that time without giving too much thought to the cost. But with a slowdown that occurred in late 2022–2023 due to increases in interest rates and other economic headwinds, the focus in boardrooms and among C-suites shifted to how — yes, how can we grow — but in a more disciplined way?

It's really interesting. I've seen some reports recently showing that mentions of cost control and operational efficiency on earnings calls are at record-high levels. As a result, organizations are being more judicious and strategic about where they hire talent and where they place these key capabilities globally.

Surely, organizations are going to want to keep those specialized AI skills and talent pretty close to home — in their headquarters cities or in cities that are centers of innovation, like the Bay Area, Toronto, London, and Paris. So, we tend to assume those are going to be the primary hubs where we see the demand growth. And those cities certainly have some of the highest demand, but their demand growth is falling yearly. If we look in the middle here, we can see their growth in the neutral or negative territory. And as you called out, John, the highest demand growth is in lower-cost markets: Pune, Hyderabad, Chennai, and Mexico City.

So what's going on here? It's not about offshoring to lower costs. This is not labor arbitrage because we're talking about highly specialized skill sets critical to organizational AI strategy. What is happening is there's this recognition that these specialized skill sets exist in these global technology hubs, which also happen to have lower costs. So there's a win-win to be had — if, and this is a big if —we can successfully compete for the talent.

‍

[0:14:37] John Lynch: Awesome. Yeah. We can get into the skills story in a bit more detail. So, when we look at the global demand, we look at how this looks in some of those key markets we pulled down in the previous slide. These kinds of sophisticated, highly sophisticated skills — PyTorch, TensorFlow — we see fairly even demand for those across all markets, which again runs against established understandings of what is available in those markets. Could you talk a bit more about that, too?

‍

[0:15:10] Lynne Mayers: Yep. Absolutely. We wanted to look at the percentage of AI engineer job postings that required more advanced machine learning frameworks like PyTorch and TensorFlow. That was how we explored the maturity of these markets. Are employers looking for those cutting-edge frameworks there?

As you mentioned, John, what we can see here is that the percentage of job postings requiring PyTorch and TensorFlow is just as high in locations like Bangalore and Hyderabad as it is in the U.S. It's almost as high in other tier-two tech cities in India like Pune and Chennai. So, it's really interesting. And Parker, in your conversations with clients, have you found that most of them realize there are opportunities to hire AI engineer talent outside their domestic markets?

‍

[0:16:04] Parker Devlin: Well, some yes and some no. I would say about half of them aren't quite there yet. They tend to focus more on the emerging hubs in North America and Western Europe. We talk about that. But of course, the reality is that all of the markets in these regions are competitive and high-cost at this point. So you might say Dallas is X percent the cost of the Bay Area, but that pales compared to the cost advantage we find in India and Mexico.

About 30 to 40% of them are starting to explore outside the U.S. They're generally inclining toward markets in India, Eastern Europe, some Central and South America, and some APAC, although less so in China than in other markets. I find they are really focused on identifying markets where the talent pools currently exist. However, They're not always prepared for the level of competition they will face once they get there. So that's where we try to focus the conversation, saying, look, we need to be prepared for fierce competition from other employers who will also be tapping into these talent pools.

‍

[0:17:28] John Lynch: Cool. I think this is a good time to shine a spotlight on some of these different markets. It's not a 100% India story, but India is a very compelling market from an AI engineering talent perspective. If we take a look here at the top employers in India — yes, for a while — what you're seeing here is a running chart of how the top employers for AI engineering talent in India have changed over the last couple of years. You'll see certain organizations that have continuously hired quarter by quarter for that specific set of skills. Over time, that overall view has changed, but it tells a pretty interesting story about the level of competition for this specific set of skills. Would you agree with that, Parker?

‍

[0:18:23] Parker Devlin: Yeah, of course. A couple of things to notice here. First, we see that the overall number of postings increases over time. In terms of who dominates the top employer list — while there are some changes in rankings over time, and some companies get added while others drop off — there's a core list of firms that remain on top. We'll see employers like Intel, Siemens, Micron, and Qualcomm.

These are organizations that clearly decided to enter the Indian labor market early on and have built up a presence and brand there.

Also, multinational firms dominate the market. There are no India-based firms in this top employer list, which tells us that large multinational organizations feel comfortable placing their AI roles in India and finding the talent they need.

The last thing I'll say is that although there are a number of large tech giants on this list — IBM, Google, Intel, Dell — we also see major chipmakers like Qualcomm and AMD, industrials like Honeywell, and even financial services like JPMC. So this tells us that even non-tech players are viewing India as a source for their AI engineering talent.

‍

[0:20:01] John Lynch: Awesome. There's a really interesting story here about competition in this market. As you call out, these are big players from many different industries. This is affecting the talent market. They're taking a slightly different approach, constantly evolving to make the most of the available skills. How are they adapting, and what's the next stage for them?

‍

[0:20:35] Lynne Mayers: Yep. So, a couple of things. First, we're seeing that employers are starting to look beyond those established talent markets, expanding in India and AI hiring into other cities. For example, you can see some of the cities in India where demand growth is the highest: Indore, for example, with 100% demand growth year over year, Kota with 47%, and Mumbai with 43%. Now, the top tech hubs like Bangalore, Pune, Delhi, and Chennai still have positive growth, but the growth rate is a little bit lower.

I want to be clear: we're not saying there are huge, untapped pools in these other cities. The established Indian tech hubs still really dominate from a talent pool size and maturity perspective. But we thought it was interesting to see some of these cities where demand growth is increasing. Because the largest talent pools are concentrated in those major tech hubs and competition is fierce, the second approach companies are taking is to make sure they have a very compelling employee value proposition to attract and retain this talent.

‍

[0:21:54] John Lynch: Amazing. I think that's the next stage of the story around how organizations adapt their approach. What are they doing in terms of compensation and benefits to attract the best talent available in the market?

‍

[0:22:11] Lynne Mayers: Yep. So, let's talk about that. First, as a backdrop, I want to acknowledge just how challenging the last couple of years have been in attracting and retaining tech talent in India. I think 2023 was a bit of a welcome respite from the frenzy of 2021–2022. Still, I know that a lot of organizations with a presence in India experienced a level of turnover, candidate ghosting, and wage inflation that was just unprecedented. Some even question the sustainability of growing in India because of the challenges of attracting and retaining talent there.

That is why our Strategic Consulting team is working to help clients design and deliver more compelling EVP globally and in tough markets like India. I do have a few clients who have shared that their turnover is manageable. It's not because, in every case, they're the most recognized technology employers in the world, but because they've invested in building an attractive EVP and, most importantly, delivering on that EVP every day. And yes, part of that is compensation, but it's just so much more than that.

For example, look at some of the EVP key attributes that Intel and Google offer here on this slide. Both are offering hybrid and remote work opportunities, which we know is highly valued by tech workers today, globally, and in India. Google offers four "work from anywhere" weeks per year; Intel offers bonus payouts five times per year. Both offer tuition reimbursement, assistance, and various forms of learning support to compete for AI talent in a crowded market. These are well-recognized brands and are taking steps to make their EVP more attractive. So, it tells us that no organization can afford to be content with a plain vanilla EVP regarding AI talent in India.

‍

[0:24:30] John Lynch: Awesome. Plain vanilla is not going to work. Suppose you make the strategic decision to look for these capabilities in India. In that case, you need to be able to design an EVP that will work in this market. Before you take that step, you need to be prepared with information on your competitors and talent. That's super interesting.

I think we've covered India a lot. I want to highlight a couple of other markets before we finish today. China is a fascinating market for different reasons. If we look back at our top 20 markets again, we see this demand growth story. This is where we're seeing some fascinating negative demand growth in China for the three major tech hubs of Shenzhen, Shanghai, and Beijing. That's a pretty interesting picture. I think many factors are at play, such as geopolitical headwinds. Could you paint that picture a little more?

‍

[0:25:53] Lynne Mayers: Yes. To be clear, China still has some of the largest markets in the world for AI talent. That's due to the government's focus on AI as a strategic priority. Their five-year plan from 2017 highlighted AI as a key growth area, leading to significant investments in AI education and research.

But we're focusing on the demand trends here. As you said, John, we're seeing notable slowdowns in the top Chinese cities. There have been a number of headwinds: the U.S. ban on chip imports to China, the fact that China lags in chip-making domestically, and the need to find workarounds to catch up.

There was also a significant slowdown in funding for AI startups in 2023, and some regulatory constraints in China have impacted the AI industry. All of this has led to a more cautious approach to hiring.

There's another part of the story that I think is really interesting. So Parker, tell us a little bit. We're going to take a look at this on the next slide. Who dominates the hiring in China, and how has their hiring trended?

‍

[0:27:10] Parker Devlin: Of course. Thanks, Lynne. Taking a look at this, a couple of things stand out. First, hiring is down year over year almost across the board. Second, in contrast to what we saw in India, the organizations that dominate hiring in China are mostly large domestic Chinese organizations such as ByteDance, Tencent, and Huawei. There are some large multinational organizations such as Qualcomm, NVIDIA, HSBC, and Apple, but it's a more mixed picture.

This is unsurprising, as we know these large organizations have been investing significantly in building their competitive AI capabilities. ByteDance, Tencent, and Huawei have all launched their large language models. But still, their hiring has decreased year over year.  

We're not seeing the kind of investment in AI capabilities in China that we did see in India.

‍

[0:28:18] John Lynch: Fantastic. The interesting thing about this particular market is that it's such a high-value skill set and has a global network of talent. We're seeing frozen opportunities in other markets and interesting observations for adjacent markets.

One of the things we noticed in our analysis was the interesting story playing out in Singapore. Again, if you think back to our overall demand picture, Singapore was kind of dead center. There's slow growth in demand, not accelerated demand. No negative growth, but interesting things are happening that are going to play out over the next several years and likely impact the labor market in the region and across the world.

‍

[0:29:12] Lynne Mayers: Absolutely. Singapore has made significant strides in becoming an AI talent hub through government initiatives and private-sector investments. Recently, the government announced a $750 million investment in AI over the next five years. There's a focus on upskilling the existing workforce through training programs and partnerships with educational institutions. A new tech visa program is also designed to attract global talent.

In fact, for several years, Singapore has been attracting more Chinese firms, specifically venture capitalists and talent. It's establishing itself as a neutral zone amid heightened U.S.-China tensions. The implication is that organizations interested in exploring Singapore should be prepared for a highly competitive market there and across APAC.

‍

[0:30:13] John Lynch: Fantastic. There are interesting regulatory changes and regional developments. With the well-established channel from the Chinese tech talent market into Singapore, it's going to be interesting to see how that plays out.

Lynne, I think we've talked a lot about our research. For our audience today, we'll expand on this research with more deliverables and interactive data tools so you can start to see this information in more detail. But I want to take a step to the side and talk about how TalentNeuron can help our audience with specific challenges, such as the AI talent challenge. Could you expand on that a bit?

‍
[0:31:08] Lynne Mayers: Yeah. So TalentNeuron provides valuable insights, as I hope we've shared today, into the global AI talent landscape. And those insights, most importantly, can be delivered across a range of solutions, including our SaaS platform on the left there, our Data-as-a-Service solutions, and our custom services, which include our Workbench reports and strategic consulting team.

For example, if you're a client, you might use our platform to track competitor hiring for AI talent and skills in real time and get a snapshot of global labor markets. If you're building out your analytic products and dashboards to support strategic workforce planning or location decisions, you might benefit from an AI or data feed that combines our data with yours to provide that strategic decision support.

Our consulting services can also connect you with experts on our team who will help you formulate your AI talent and location strategies and provide tailored, data-driven recommendations based on your specific needs. So that's just one example.

‍

[0:35:21] John Lynch: Amazing. Thank you. I think, like I said at the beginning, if anybody would like to find out more about how we can help in your situation, your scenario, there are many ways we can customize what we do to fit specific needs.

You can request the demo here at the top of the screen. We'd be happy to continue the conversation and have someone contact you.

In the meantime, I think it's a good time to get to some of the questions we've been collecting throughout this session. I appreciate that we've covered a lot of ground here, so there are probably many questions.

We talked a lot about India and China. Still, in our earlier graphic, we had a few other particularly interesting markets. Somebody's asking here: Can we talk about some of the other emerging markets for AI talent? What else are we seeing in Eastern Europe, perhaps Latin America? I'll open that up to both of you.

‍

[0:33:24] Lynne Mayers: Yep. Sure. So we do see that there are other markets that are strong for AI engineering talent. We did talk a lot about India because it has several of the largest markets in the world.

But Mexico City, São Paulo, Warsaw, and Budapest are other markets with really robust talent pools. And while those talent pools aren't quite as large as those Tier 1 cities in India, the compensation isn't quite as high in some cases either.

With TalentNeuron's data and decision support, we can help clients weigh multiple factors as they make global location decisions and determine the right market for their needs and how to craft that strategy.

‍

[0:34:17] John Lynch: Fantastic. I think we have time for maybe one or two other questions. Let's see how we do with this one.

So, this is a specific advisory-type question, consulting procedure: What are some effective strategies for upskilling to meet this growing demand for AI skills?

‍

[0:34:39] Lynne Mayers: Yeah. It's a great question — especially because of some of the shortages in talent relative to demand. What we see is that a lot of progressive organizations are looking for ways to pivot to building, not just buying, the talent they need.

This is going to become increasingly critical. The first thing we see progressive organizations doing is thinking about how to build AI capabilities by putting together the right teams of people rather than trying to find everything in one kind of magical individual.

How do I break down the need into complementary capabilities and hire people who can combine different pieces of the puzzle?

Second, we really need to move fast with upskilling. We do not have time for extended training programs. Organizations are using TalentNeuron's skills evolution insights to get ahead of growing and emerging skills and identify adjacent skills in their organization to upskill talent.

They're also looking to redeploy talent from different parts of the organization that might have the skills; maybe there are people in operations, supply chain, or finance with the needed capabilities.

To return to the skills adjacency example, maybe I don't have someone deeply versed in PyTorch, but they are a Python expert. I can more readily upskill that person.

So those are some of the strategies we see organizations using on the build side, John.

‍

[0:36:36] John Lynch: Okay. I think there are actually a couple more questions, specifically on the skills piece. I'll address those a bit myself.

There's a question about emerging skills, which I think we touched on earlier with PyTorch and TensorFlow. For everyone attending this webinar, in the follow-up, you will receive a sneak preview, basically an early view of our research asse,t which includes all the information on those emerging skills.

You'll see, in more detail, the types of skills we see increasing in demand and which organizations are hiring for them, particularly in AI engineering. So keep a lookout for that, and congratulations on making a good decision by joining this webinar and getting that sneak peek.

Parker, is there something you'd expand on?

‍

[0:37:29] Parker Devlin: Yeah. Of course. Thanks, John. Understanding table stakes skills, including sci-kit-learn, Hugging Face API, and deep neural networks, is important.

But, as you mentioned, there's also a need for more advanced, emerging skills like large language model training, fine-tuning, merging, and stable diffusion model training.

Excited to see that list in the follow-up.

‍

[0:38:01] John Lynch: Cool. Awesome. And thank you so much for your questions, everybody. If we don't get to them right now, we'll follow up afterward.

We really appreciate your time; thank you for joining us.

And Lynne and Parker, thank you so much for sharing your insights.

We have more webinars coming up next month that will expand on today's discussion, including other topics such as how to apply EVP and encourage skills-based learning within your organization.

I hope we'll have you back for those. Please keep an eye out for the registration link.

In the meantime, thank you again, Lynne and Parker. It's been a pleasure. Until next time.

‍

[0:38:51] Parker Devlin: Thank you, John and everyone.

‍

[0:38:53] Lynne Mayers: Thank you, John.

‍

[0:38:54] John Lynch: Thank you.