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Talent Market Pulse
In 2024, job postings around the world went up by 61%.
Expected hiring gap because there are not enough workers.
There is a huge demand for AI and ML talent right now. Job postings went up by 61% around the world in 2024, which is a lot more than the 1.4% rise in all jobs. This was on top of the 80% rise in 2022–23. There are not enough qualified professionals to meet this need, which will create a 50% hiring gap, fierce competition among employers, and a clear seller’s market. A talent gap occurs whenever the labor market cannot produce enough qualified candidates to keep up with rising employer demand, and this is exactly the situation the artificial intelligence industry is in right now. This talent gap is present in almost every field that uses digital technologies and AI.
AI users get almost five times as much work done, but 40–50% of executives say that not having enough skilled workers is a big problem for AI implementation. When you outsource work, it costs more and is more likely to make mistakes or lose security when it is sensitive. This is because there are not enough skilled workers. This skills shortage ultimately hampers economic growth, productivity, and innovation in a way that goes beyond just being a problem for human resources. HR leaders are starting to realize that being able to bridge talent gaps is key to a company’s goal of staying competitive.
In 2023, the AI job market slowed down for a while, partly because tech companies were laying off workers. But it came back strong in 2024, and AI hiring started to rise again in most countries.
Generative AI tools like ChatGPT came out in late 2022, which led to a new wave of hiring and investment in skills related to generative AI that lasted through 2023 and 2024.
The number of U.S. job postings that asked for “generative AI” skills went from 16,000 in 2023 to more than 66,000 in 2024.
More and more companies are hiring people for jobs that require AI. They expect people who work in software development, product management, and analytics to know about AI, not just people who work in pure AI research jobs.
To sum up, the global AI talent gap got bigger between 2024 and 2025 because demand rose quickly, supply did not keep up, and businesses’ stakes got higher. More and more C-level executives and investors are realizing that closing this gap through aggressive recruitment, training, and pipeline development is necessary to stay ahead in an AI-driven economy.
Almost one in four new tech job ads asked for AI skills by 2024. This was two times as many as in 2022. Before, companies mostly hired people who were experts in AI, such as machine learning engineers and data scientists. They now want people who work in a lot of other common tech jobs, like software developers, business analysts, and project managers, to use AI every day too.
Most of the time, companies want AI experts who have worked in the field for a long time. There are some entry-level AI jobs, but they are not as common and are usually only available to new graduates. Because of this, all kinds of businesses are looking for people who know how to use artificial intelligence. It used to be important to know how to code, but now it is just as important to know the basics of AI. Businesses need to look at job roles, pay scales, and chances for career growth again because of this change. There are now skill gaps in every area of the labor market, not just in a particular job that requires specialized research.
There was a sudden, huge rise in the need for people with very specific AI skills when large language models, new AI tools that can make content, were made public in late 2022. In the US, the number of job ads that asked for “generative AI” skills went up four times in just one year. By 2024, there were over 66,000 job ads like this. This also led to the creation of new jobs, like those that work on improving these AI models or using them to make content.
It is important to remember that these skills are not only useful for research teams. A lot of different departments are now looking for people who are experts in their field and know how to use generative AI. LinkedIn said that being able to make and use these big language models will be the software skill that grows the fastest by 2025. Natural language processing, deep learning, and reinforcement learning are some of the most in-demand new skills in the job market today.
All of the big industries are hiring more AI workers, but the way and speed that they do recruitment are very different from one sector to the next. Technology companies still hire the most AI workers, but the gap is quickly closing in on financial services and healthcare. These industries use AI systems for things like fraud detection, risk management, decision making, and medical diagnostics.
Industries that use AI wisely are seeing their workers’ productivity rise by almost five times, which leads to more hiring and investment. The public sector and schools have been slower to add to their AI teams, but they are still hiring more AI professionals.
The need for certain AI jobs is also changing as more businesses use AI. There is still a high demand for core machine learning engineers and data scientists, but there is also a growing need for people with new skills. There are MLOps (Machine Learning Operations) engineers who make sure AI systems work well, AI reliability engineers, AI product managers, and AI ethicists. This shows that the focus has changed to making sure that AI is used responsibly in the real world.
Businesses still do not have enough skilled AI workers to meet their needs. Almost 20% of people who get a PhD in computer science in the U.S. now work on AI. That is almost twice as many as ten years ago. Also, every year, tens of thousands more people learn about AI through college undergraduate programs, MBA courses, PhD programs, and industry certifications from companies like Google and Microsoft.
Still, not many AI experts are really ready for tough job duties. Businesses are facing a widening gap. On one hand, a lot of new people are getting into the field with certificates. But there are not as many experienced engineers, data scientists, and research PhDs who can get results right away and lead projects. As the skills needed to work with AI change quickly, continuous learning and keeping the current workforce trained and up to date is becoming just as important as hiring new people.
Most of the world’s AI experts work in only a few important places. The US is far ahead, with about 60% of the world’s top AI experts and major AI centers located there. China also makes a big difference by doing a lot of research and turning out a lot of AI graduates. But a lot of its experts go to the U.S. or Europe to find better jobs. India’s large number of STEM graduates is also quickly increasing the number of people who know how to work with AI.
Along with these big players, a few smaller countries also have great AI skills for their size. For instance, Israel has the most AI professionals per person, and Singapore and Luxembourg have a lot of AI job openings. There are active AI communities in the UK, Germany, and France, but their talent is more spread out than in the US. Toronto, London, and Shenzhen are still popular AI hub cities because they have a lot of research, funding, and big companies that hire people. Other countries are also putting a lot of money into building their own AI skills so they can compete for talent around the world.
Companies can now hire AI experts from all over the world in new ways thanks to distributed work models. Companies can now hire experts from places like Eastern Europe, Latin America, and Africa, which often saves them money. Because of this, multinational companies are opening more and more satellite AI centers in cities like Warsaw, Buenos Aires, Montreal, and Nairobi to get skills that are hard to find or too expensive at their main offices.
This is happening, but many remote jobs still have geographic restrictions that make it hard for people from certain countries or time zones to apply. This shows that being close to operations and having the right legal frameworks are still important. Also, a lot of smart people are still going to top research institutions and better-paying jobs. This is especially true since many international graduates are moving to well-known U.S. AI centers.
National governments are taking strategic steps to bring AI experts to their countries and keep them there in response to these changes in the global talent market. Targeted visa programs, research grants, and retention bonuses are some of the things that will help. Countries like Canada, the UK, China, and the UAE are doing a lot of this.
The pay for AI professionals around the world is very different, just like the differences in buying power and the level of competition.
The top spots are U.S. tech hubs. In San Francisco, New York, or Seattle, a mid-career machine-learning engineer can now make between $140,000 and $180,000 a year. The highest-paid principal research scientists at the biggest platforms can make $300,000 in cash and more than $500,000 in stock.
Western Europe pays well, but it is 30–50% less than what engineers in Berlin or Paris make, which is between €70,000 and €120,000. Prices are lower there, and the market is still growing. Asia is even bigger. In China’s big tech hubs, mid-level workers make CN¥300,000 to ¥500,000 ($45,000 to $75,000). This is competitive at home, but it is only a small part of what the U.S. spends. Japan and Korea pay between $50,000 and $70,000. India is still the best place in the country to find ML engineers. They usually make ₹1–2 million ($12,000–$24,000), but when they join distributed teams, they often get paid at global rates.
These spreads help with cost-arbitrage strategies. Companies open satellite AI centers in Warsaw, São Paulo, or Bangalore to save more than 50% on labor costs. However, the best workers tend to go to the highest bidders. Remote work is making the differences at the edges less clear, but for now, pay still follows a clear “Silicon Valley premium” curve.
One clear effect of the lack of AI talent is that salaries are rising quickly. There are not many AI/ML experts out there, so companies are fighting over them. As a result, skilled workers get high pay and great benefits. In this part, we look at how much people are paid right now and how that changes from one region to the next:
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