Who Wants to Be an AI Millionaire?

The artificial intelligence boom isn’t just about launching the most advanced AI models, it’s also about securing the top AI researchers and engineers capable of building them. And over the last several months, compensation for this elite cohort of talent has gone through the roof, with companies offering packages worth hundreds of millions (or even billions) of dollars to lure them away from competitors.

Many of these massive payouts have come from Meta, with CEO Mark Zuckerberg poaching some of the field’s brightest minds to beef up its new Meta Superintelligence Labs, a division the company dedicated to building superintelligent AI. Zuckerberg’s hiring wishlist — known simply as “The List” among industry insiders — includes people who have worked on breakthroughs like ChatGPT and GPT-4. And the offers they’re receiving are staggering, reaching tens — or in several cases, hundreds — of millions of dollars for a four-year contract. In one case, Zuckerberg reportedly offered Andrew Tulloch, a former OpenAI researcher and cofounder of Thinking Machines Lab, a compensation package exceeding $1 billion, but Tulloch turned it down.

And Zuckerberg isn’t the only one pulling out all the stops. According to the Wall Street Journal, OpenAI CEO Sam Altman has courted recruits over poker games and private dinners at his San Francisco mansion. And Elon Musk once threw a recruiting party at OpenAI’s former headquarters to woo researchers over to his rival startup xAI. For those at the forefront of the AI arms race, acquiring top talent has become a zero-sum game, with everyone fighting over the same small circle of people.

Indeed, the lavish parties and multi-million-dollar offers underscores just how frothy the AI talent market has become. But behind the spectacle lies a very obvious question in today’s increasingly competitive job market: How does one become the kind of AI professional that tech giants fall over themselves to hire?

Built In analyzed the educational and professional backgrounds of 38 AI researchers and engineers who recently switched jobs (mostly to Meta) to better understand what makes them so valuable. Here’s what we found, and what strategies you can apply to your own career.

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What AI’s Highest-Paid Experts Have In Common

There are several commonalities among the AI researchers being targeted by Zuckerberg and other tech CEOs. More than 60 percent of the recent recruits Built In looked at hold PhDs in computer science, with Stanford University producing the largest share, followed by Massachusetts Institute of Technology (MIT) and Carnegie Mellon University — extremely selective institutions that have become pipelines to the nation’s top AI shops. Many of these researchers carved out an expertise in niche AI subfields, such as computer vision, speech-to-text or multimodal processing, and built their reputations working on the teams behind landmark models like OpenAI’s o1 and Google’s Gemini. Nearly all of them have published influential research papers cited by other AI researchers as well.

Another trait shared among today’s most coveted AI talent — and one far harder to replicate — is timing. Many of these researchers began their doctoral work more than a decade ago, exploring topics like robotics, generative AI and neural networks at a time when they were regarded as little more than esoteric, academic curiosities. Now, those same fields are driving the AI boom, and the people who mastered them early are cashing in.

How Do I Become an AI Millionaire? 

Of course, you can’t jump in a time machine and start a PhD in neural networks 15 years ago — the particular ship that carried today’s AI millionaires to success has basically sailed. But there are still concrete steps you can take to build a successful career in artificial intelligence.

Learn the Fundamentals

First and foremost, master the fundamentals. Calculus, linear algebra, probability theory and statistics form the foundation for nearly every AI concept, and will remain essential no matter how the field evolves. During a presentation at SXSW London in June 2025, Google DeepMind CEO Demis Hassabis said students should study math, physics and computer science, noting that these disciplines teach the kind of problem-solving, logical reasoning and analytical thinking skills that will be essential in shaping the future of AI.

“They’re not just essential for understanding AI — they’re essential for thinking clearly and solving the world’s biggest challenges,” Hassabis said.

Equally important are subfields like machine learning, deep learning and natural language processing, as they form the backbone of most modern AI research. In terms of programming languages, the most important one to know is probably Python, which underpins popular machine learning frameworks like TensorFlow and PyTorch.

Get a Doctorate 

A doctorate degree isn’t always required to land a prestigious job in AI. After all, Alexandr Wang — who now serves as Meta Suprintelligence Labs’ chief AI officer after it invested $14.3 billion in his startup Scale AI —  dropped out of MIT before even receiving his bachelor’s. Still, most of the researchers being scooped up right now graduated from elite universities, and studied under the tutelage of respected professors who specialize in an AI-related field. Stanford, MIT, Carnegie Mellon and the University of California, Berkeley are some of the most popular schools, followed by the University of Southern California and the University of Illinois Urbana-Champaign.

Get Some Hands-On Experience

Internships are another key entrypoint, particularly those offered at prominent AI research companies like Google, Microsoft and Amazon. While some are open to undergraduates, most target master’s and PhD students. They’re highly competitive, but candidates can set themselves apart from the crowd by getting strong referrals and showcasing their projects on sites like GitHub, where much of the top AI talent publishes and shares work.

Antje Barth, principal developer advocate for generative AI at AWS, told Built In that successful AI practitioners often have a strong foundation in computer science, mathematics or engineering coupled with hands-on experience building and deploying real AI systems — either through academic research, industry experience or open-source contributions.

“Regardless of their path, what sets these practitioners apart is their direct experience bridging the gap between theory and practice,” she said. “They've all faced the fundamental challenge of moving from a model or AI application that works in research to one that performs reliably in production.”

For AI developers interested in getting into the field, Barth recommends going beyond tutorials and working with real world data to understand what works in practice, not just in theory. They should also experiment with agent-based AI systems, using open source frameworks like Strands Agents SDK and protocols like MCP and A2A. Finally, and perhaps most importantly, junior developers should master the art of context engineering, or understanding how to provide AI systems with the right information, in the right format, at the right time. 

“Context engineering is especially important in multi-agent environments where systems need to interact and collaborate,” Barth said. “The most successful developers will be able to design context effectively, understanding how to guide AI systems toward desired outcomes while maintaining efficiency and safety at scale.” 

Publish Your Work

Visibility is also important. Beyond coursework and internships, aspiring AI professionals should try to actively contribute to the field and make their work accessible. 

Many of the industry’s most sought-after researchers presented papers at well-known conferences like NeurIPS (Conference on Neural Information Processing Systems), ICML (International Conference on Machine Learning) and ICLR (International Conference on Learning Representations), where the most cutting edge AI research is discussed. Others contributed to open-source projects like TensorFlow or Scikit-learn, and shared their work on GitHub and similar sites. These efforts not only demonstrate technical expertise, but they also help establish credibility and ensure your work gets noticed by both peers and potential employers.

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The Shifting AI Talent Landscape

While a handful of AI researchers are commanding nine-figure paydays, the reality for most software engineers looks very different. Demand for generalist developers — especially those just starting out — has collapsed. Companies are tightening their talent budgets, and AI coding tools are now used to automate many tasks that once required junior engineers. Even if you follow the exact path of today’s AI millionaires, the broader job market is no longer built to reward newcomers in the same way.

The data says it all: Software development job postings on Indeed have dropped by more than 70 percent since March 2022. Recent college grads with a computer engineering or computer science degree are more likely to be unemployed than those with degrees in communications, history and even liberal arts, according to 2023 data from the Federal Reserve Bank of New York. While mid- and senior-level roles have largely bounced back since the 2023 tech market downturn, the number of entry-level positions has stayed stagnant. A 2025 report found that only 7 percent of new hires at the 15 largest tech firms were recent grads — a more than 50 percent drop since 2019. Newly minted computer science professionals are increasingly turning to skilled trades, sales roles and restaurant work to make ends meet after months of unsuccessful job hunting.

For computer science majors pursuing a career in AI, the bar is rather high. Coursework in  subjects like machine learning and deep learning, as well as AI-oriented math classes like linear algebra and probability theory, is a must. And an advanced degree, particularly a PhD, can significantly improve the odds of landing an internship or job at a top AI lab. 

But getting in is only the beginning. Artificial intelligence is advancing at breakneck speeds, which means the professionals working in the field are expected to stay current, reading the latest research papers and engaging with the broader AI community. Continuous learning isn’t optional, it’s the only way to stay relevant as the technology and its applications keep pushing into new territory. 

“The strongest practitioners exhibit a persistent drive to learn that goes beyond technical expertise — a combination of curiosity and pragmatism,” Barth said. “They maintain an appetite for learning even after years in the field. They view rapid changes in AI as opportunities to grow and innovate. They dive deep into code, identify limitations and constantly question how to improve.”

They should also be able to work effectively across different teams and make decisions based on data instead of assumptions, Barth said.

So, while the demand for AI professionals has never been higher, the expectations — particularly for positions at the world’s top tech companies — are just as high. Future AI researchers and engineers can look forward to opportunities that are exciting, challenging and continually evolving. But they shouldn’t do it for the promise of a billion-dollar paycheck.

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