Watch an interview with the authors of this article to learn even more! (Video 1).

Giving Decent Jobs to Everyone

The United Nations created the Sustainable Development Goals (SDGs), a set of 17 global targets, to make our world a better place for everyone [1]. These goals cover many important areas, including ending poverty, protecting the planet, and ensuring everyone can access quality education and good jobs. One of these goals, SDG 8: Decent Work and Economic Growth, is about the importance of work. Work is not only how people earn money to support themselves and their families—it also gives people a sense of purpose (a reason to get up in the morning), dignity (feeling proud of what they do), and a chance to contribute to society. SDG 8 focuses on making sure everyone can have a safe, fair, and rewarding job while helping the global economy grow. To make this happen, companies must be productive—meaning they need to efficiently create more goods or offer better services. When companies are productive, they can grow, create more jobs, and provide better opportunities for everyone. Another part of SDG 8 is ensuring that everyone who wants to work can do so, earning a fair wage in a safe and healthy environment. It also calls for protecting workers’ rights, so no one is treated unfairly or put in danger while doing their job.

You can think of all the SDGs together like a giant puzzle. Each piece is important on its own, but they only create the full picture when they connect. For example, SDG 9 (Industry, Innovation, and Infrastructure) supports SDG 8 by promoting new technologies to improve the economy, while SDG 4 (Quality Education) helps by making sure people have the skills necessary to do their new jobs. Together, all SDGs fit like puzzle pieces to build a future where everyone can thrive.

Using Science to Understand Economies

Economics is the study of how money, jobs, and resources are shared in society, and it can be really complicated—but science provides several tools to help people understand it better. Some of the most useful tools are mathematical models, which work like a “practice world” where scientists can test ideas without risking real-world problems. For example, we can use models to help find out how many doctors, teachers, or engineers a country will need in the future, helping schools prepare the right number of students for these jobs. Models can also test what might happen if a country changes its minimum wage (the minimum pay anyone should earn on a job), showing whether the change would help workers earn more or cause businesses to hire fewer people. Science can also help by showing the big picture of how the economy works. One of the coolest tools we use for this has a very weird name: a mean field game (MFG). Do not worry, it has nothing to do with mean people playing games in a field!

Imagine you are standing on a balcony high above a busy train station (Figure 1). From where you are, you cannot follow the movements of each individual person—you do not know exactly where that man with the dog will end up or which train that senior woman will take. But you can see the overall movement of the crowd. You notice that a set of people are walking toward the platform, a smaller group is heading to the ticket machines, and others are slowly making their way to the exits. You can also see when the station starts to get crowded and when it becomes quiet again.

This is similar to how scientists use MFG models [2, 3]. In economics, instead of following every single company in detail, we look at the overall patterns of thousands of companies at once. Like in the train station, we want to understand the “big picture”. For example, we might see that many companies are now offering remote working options: people work from home, use their computers to do their jobs, and talk to colleagues though the internet—instead of working at the office in person. We can track how quickly these changes happen and what patterns appear over time, without needing to watch each company one by one. This big-picture view helps us think about the future of jobs.

Artificial Intelligence—The Perfect Employee?

You have probably heard of artificial intelligence (AI)—the technology that is reshaping our world. Simply put, AI trains computers to find patterns and solve problems in ways that seem human-like. That means a computer can recognize faces in photos, translate languages, drive cars, or help doctors spot diseases more quickly. For companies, AI can mean faster production, fewer mistakes, and the ability to analyze huge amounts of information in seconds. For the job market, AI has mixed effects: it may replace some simple, repetitive tasks that people do today, but it can also create new jobs—such as designing AI systems, maintaining them, or using them in creative ways to solve problems.

Imagine you owned a company. AI might seem like the ultimate employee: it never gets tired, never takes vacations, and can work all day and night. Sounds perfect, right? Well, it is not that simple. Setting up AI systems takes a lot of time and costs a lot of money. Even more challenging, when AI does jobs that were once only done by people—whether they are simple repetitive tasks (like sorting emails) or more advanced work (like analyzing scientific data, drafting legal documents, or helping doctors design treatment plans)—it can push out workers who rely on those roles to earn a living. This can leave many workers in a tough spot, making it harder for them to support their families. This is a huge challenge that society needs to solve.

Forecasting Tomorrow’s Jobs With MFGs

At KAUST, the Mean Field Games and Non-linear PDE Research Group investigates how mathematical models, including MFGs, can help to address these important social, economic, and technological questions [1, 2]. One of our current projects, in partnership with scientists from Durham University in the United Kingdom and Aix-Marseille University in France, focuses on a big question: how do companies decide whether to keep their old ways of working or invest in AI?

Using MFG models, the group can build a “virtual economy” where every company is trying to make the smartest move. In the model, each company’s decision is influenced by the average behavior of the whole economy, just like in the train station. For example, a company might decide to adopt AI if the average wages of its employees become too high, making AI cheaper in the long run. If thousands of companies make similar choices and replace some jobs with AI, this can change the average pay of workers across the entire economy. This happens because AI systems can take over certain tasks, reducing the number of jobs and lowering pay for people doing that kind of work. At the same time, people who know how to build, manage, or use AI may earn more, since they help companies save money by relying on fewer workers. Our model looks for a balance point and allows us to test different scenarios: what happens if many companies adopt AI quickly? What if most of them wait? And how does this affect competition between companies?

To explore these questions, we looked at two types of companies: traditional companies that use regular machines and methods and can easily hire or reduce workers; and companies that decide to bring in AI (Figure 2). However, adopting AI comes with what is called a fixed adoption cost—money needed for research, new equipment, and training workers to use the technology.

Think about the train station again. You see a new, super-fast train that promises to get you to your desired station much quicker. But the ticket for this new train is much more expensive than the regular one. Many passengers (just like companies) decide to wait. They watch to see if the fast train is really worth it, or if the price will go down later. This “waiting period” is just like the pause some companies take before adopting AI.

By answering these questions, we can understand why AI can have mixed effects on the economy. Sometimes it makes simple jobs disappear, which explains why wages can drop when AI is first introduced. But it can also create new kinds of work—like jobs for people who design or maintain the “fast trains”. AI can even help workers enjoy more free time by making the workday shorter.

These results are important for companies, but also for the people who make the rules in countries. Governments can use our research to decide when to encourage companies to adopt AI and how to protect workers at the same time. This way, technology can help economies grow while making sure its benefits reach everyone. So, the next time you wonder if math is useful outside of the classroom, remember this: it is a powerful tool that helps us build a smarter and fairer world for everyone!

You Are Part of the Future Economy!

SDG 8—Decent Work and Economic Growth is essential to make sure that everyone has access to safe, fair jobs and that economies grow in a sustainable manner. At KAUST, our research uses mathematical models, like MFGs, to explore how companies decide whether to use AI and how those choices affect jobs, wages, and the balance between work and free time. By understanding when and how companies invest in AI, we can help decision-makers design rules and plans that protect workers, support fair pay, and keep economies strong. Our research supports SDG 8 by showing that technology, when used wisely, can create new opportunities and new kinds of work.

This is where you come in! The jobs of the future—many that have not even been invented yet—will one day be yours. The things you learn today will give you the power not just to do those jobs, but to create them and make sure they help everyone, not just a few people. If you stay curious, keep learning, and use your ideas to make the world better, you will be helping to build a future where technology and people work hand in hand, and where society is fairer and stronger for all.

Glossary

Economy: ↑ The system of how money, jobs, and resources are shared between people, companies, and governments.

Productive: ↑ Able to make a lot of work, products, or goods in a given amount of time, using people, machines, or technology.

Mathematical Models: ↑ “Practice” worlds built with math to test ideas and predict what might happen in reality.

Mean Field Games: ↑ Math tools that study the big patterns of many players or companies, where each is trying to make the smartest possible choice based on what the whole crowd is doing.

Artificial Intelligence (AI): ↑ Technology that allows computers or machines to learn from data, recognize patterns, and make decisions, so they can help people or do tasks on their own, almost like humans do.

Job Market: ↑ The world of available jobs, where workers look for work and companies look for employees.

Fixed Adoption Cost: ↑ The one-time money a company must pay to set-up or start using a new technology, like buying machines or software, no matter how much it is used.

Conflict of Interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Nicki Talbot for her invaluable support during the development of the Collection, without which it would not have been possible. We also acknowledge Ana Runte for creating the illustrations featured in this article. We further extend our gratitude to the KAUST Office of Sustainability and the UNDP Saudi Arabia Country Office for their dedication to raising awareness of the UN SDGs in our journey toward a more sustainable world.

AI Tool Statement

The author(s) declared that generative AI was used in the creation of this manuscript. The authors acknowledge the use of ChatGPT (GPT-5.2), a generative AI language model developed by OpenAI, to support editing for clarity and structure, and evaluate the readability of this manuscript.

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