The new gold rush
The tech world has never seen a hiring frenzy like today. Artificial intelligence didn’t arrive merely as “another trend” but as a real engine for economies and companies. A full-blown hunt for AI talent has begun on the global market. At the center of this race are the largest IT giants: Google, Microsoft, Amazon, Apple, OpenAI, and, of course, Facebook, which is increasingly referred to simply as Meta.
If just ten years ago the phrase “artificial intelligence” was associated with research labs and debates about the future, today it means real money—billions of dollars in investment and the fate of companies. Programming languages or frameworks are no longer the most expensive resource; human minds are—people capable of inventing and deploying algorithms that change the reality we’re used to.
Civilization-scale ambitions
Meta has repeatedly proven it can spot trends and invest “in the future.” After the rebrand and massive investments into the metaverse, Facebook found itself in a situation where the stakes only grew. But the market made a new turn instantly: after the release of ChatGPT at the end of 2022, a true AI revolution began. The world saw that large language models (LLMs), generative AI, and self-learning systems are not science fiction but a new digital “steam engine.”
Meta quickly realized: the winner won’t be the one with more servers, but the one with better people. The biggest corporations and startups worldwide began not just hiring, but poaching—buying out and sometimes literally “stealing” the best specialists in artificial intelligence. Facebook set a new bar in this race by offering AI stars contracts that until recently seemed impossible even for Fortune 500 top executives.
Millions for brains
Even before 2022, the AI talent market was already extremely overheated. Top specialists could already earn $500K–$1M per year (including stock, bonuses, and options), work remotely, and choose from a dozen offers. But 2024–2025 changed everything.
The biggest “nameplates” are universities like Stanford, MIT, and Oxford; labs such as DeepMind, OpenAI, Anthropic, and Google Brain; and startups like Scale AI, Cohere, and Runway. Every graduate with a serious background in machine learning, deep learning, and reinforcement learning is a potential millionaire. But even among them there are “superstars”: authors of breakthrough algorithms, architects of the largest LLMs, and creators of new optimization and generation methods.
Facebook began poaching such people, offering sums that sound like lottery winnings: $100–250 million (!) to make the move, including options, bonuses, guarantees, and signing incentives. Some experts compare this to football transfers—except now the world’s main “players” aren’t in stadiums; they sit in data centers, write code, and design model architectures.
Standout examples of “AI transfers”
- 1. Zhuoming Pang — $250M
A legendary AI architect from Apple, who stood at the origins of generative networks for mobile devices. Facebook offered him an unbelievable package to lead a new direction: personal LLMs for messaging apps and the metaverse. - 2. Alexandr Wang — $200M
The founder and “face” of Scale AI, one of Silicon Valley’s youngest billionaires, became Meta’s chief advisor on infrastructure and training models on petabyte-scale data. - 3. Trapit Bansal — $100M
An OpenAI star who worked on optimizing distributed training for large language models. His departure is one of the year’s most discussed “moves.” - 4. Nat Friedman — $100M
The former GitHub CEO and a technology visionary known for his investments in AI startups, moved to Meta in the role of Chief AI Product Officer. - 5. Daniel Gross — $50M
One of the co-founders of Safe Superintelligence, a mentor to dozens of young AI teams, and a key Silicon Valley angel investor.
All of these specialists were invited on terms that have never existed before in the history of IT: we’re talking about signing bonuses, Meta stock options, and the freedom to build their own teams by recruiting the best people on the market.
Why AI talent is the new oil field
AI today is the new oil. Just as 20th-century oil created the power of companies, AI talent now determines who will own the digital future. Platforms no longer compete only on infrastructure—almost everything can be rented or bought. The only thing you can’t buy is brains, creativity, and vision. The most powerful GPU clusters, the most expensive data centers, and massive volumes of data—without talented specialists, all of that is just “sand.”
The companies that win this race will secure decades of technological and economic leadership. It’s no accident that Facebook, Google, and Microsoft now compete for “AI team building” as fiercely as they once did for the advertising market.
Prices we’ve never seen before
Back in 2019, paying $500,000 for an engineer in the US was considered outrageous. By 2025, sums like that no longer shock anyone: if a specialist truly drove innovation in the LLM space, worked with the largest language or vision models, and took part in cutting-edge experiments with self-supervised learning, reinforcement learning, or AIGC (AI-Generated Content), they can be offered just about anything.
The situation has reached the point where major venture funds (Sequoia, Andreessen Horowitz, Y Combinator) now not only invest in teams, but also deliberately “buy out” developers for their portfolio companies. Startups race to announce record salaries just to secure an “engine of growth.”
What is changing in the industry?
1. Facebook (Meta) declares its ambition to become the No. 1 AI company
Meta has long been more than just a social network. Over the last 3 years, the company has poured tens of billions into the metaverse, AR/VR, generative models, and data-center infrastructure. The current bet is to make Facebook and Instagram the “smartest” platforms: content generation, recommendations, moderation, search, and communications.
Enormous resources are being directed toward overtaking OpenAI and Google in the quality and accessibility of AI services for everyday users. The company says openly: “Without the best AI talent, we will lose this race.”
2. Leadership is not in infrastructure, but in people
Today, cloud, compute, and data centers are “commodities.” The real value lies in those who invent new algorithms and see how to squeeze more out of hardware and data than competitors can. Meta is poaching not just developers, but architects, research leads, theorists, and people who understand how to scale and deploy AI into real products with audiences in the billions.
3. The salary market: a new reality
AI developers today are the most expensive specialists in the IT market. Salaries for Senior Machine Learning Engineer, Deep Learning Scientist, and Chief AI Officer roles start at $300,000 per year and very quickly grow to a million and beyond. As the market capitalization of Meta, Microsoft, Amazon, and OpenAI increases, the equity component can exceed the plain “cash” salary by multiples.
4. Explosive growth in startups
Almost every AI specialist who gets laid off or moves to Meta, OpenAI, Google, or Microsoft creates their own startup within a year—or becomes a co-owner of a new “unicorn.” Money is flowing freely, the market is not saturated, and demand for skills is only growing.
5. The changing role of universities and open source
Stanford, MIT, Berkeley, and Oxford are becoming not just “talent factories,” but centers of real applied science and startups. The best AI talents now work in industry while also teaching, or they mentor their own research teams.
Open source is becoming the main source of new ideas and reputation: every breakthrough project on HuggingFace, GitHub, or Papers with Code is instantly “snapped up.” Contributor experience in open source is now valued more highly than any formal diplomas.
What this means for everyday programmers and the job market
- 1. The entry bar is higher—but there are more opportunities
Yes, the battle for AI talent is happening at the very top, but it opens new paths for everyday developers as well. Anyone who can learn, adapt, and master new approaches can become part of this wave. Thousands of openings in machine learning, data science, DevOps for ML, MLOps, and AI products in C#, Java, Python, Go, and Rust appear every day. - 2. It’s not tenure that gets rewarded, but results and open-source reputation
For AI startups, large companies, and investors, what matters far more today is not the number of years on a resume, but real impact: successful projects, publications, open-source contributions, and solving hard problems. If you want to enter the AI space, start building mini-projects, publish your ideas, and participate in Kaggle and other competitions. - 3. It’s never too late to learn
The industry already has dozens of stories of people switching from adjacent professions into AI in a couple of years—starting as Junior Data Scientists, then becoming Senior, and in 3–4 years reaching the point of managing small teams. “Lifelong education” is not a slogan—it’s the market reality. - 4. The return of the “cult of personality” and “geniuses”
With each passing year, standout individuals matter more and more—both in science and in engineering. Facebook (Meta) intentionally builds its culture around “AI stars”: freedom of research, the right to make mistakes, a budget for experiments, and—most importantly—the ability to build teams themselves from the best specialists.
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