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 severely overheated. Top specialists could earn $500K–$1M per year (including stock, bonuses, and options), work remotely, and choose among a dozen offers. But 2024–2025 changed everything.
The biggest “nameplates” are universities like Stanford, MIT, and Oxford; labs like DeepMind, OpenAI, Anthropic, and Google Brain; startups like Scale AI, Cohere, and Runway. Every graduate with a serious background in machine learning, deep learning, reinforcement learning—these are potential millionaires. But even among them there are “superstars”: authors of breakthrough algorithms, architects of the largest LLM models, and creators of new optimization and generation methods.
Facebook started poaching such people, offering sums that sound like lottery winnings: $100–250 million (!) to switch, including options, bonuses, guarantees, and signing bonuses. Some experts compare this to soccer transfers—except now the world’s main “players” don’t sit in stadiums; they sit in data centers, write code, and design model architectures.
Notable examples of “AI transfers”
- 1. Zhuoming Pang — $250M
A legendary AI architect from Apple who was at the origins of generative networks for mobile devices. Facebook offered him an incredible package to lead a new direction: personal LLMs for messengers and the metaverse. - 2. Aleksandr Wang — $200M
The founder and “face” of Scale AI, one of Silicon Valley’s youngest billionaires, became Meta’s key advisor on infrastructure and training models on petabyte-scale data. - 3. Trapit Bansal — $100M
An OpenAI star who worked on optimizing distributed training of 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 investments in AI startups, moved to Meta to take on 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.”
All these specialists were brought in on terms never before seen in the global IT industry: signing bonuses, Meta stock options, and the freedom to build their own teams from the best people on the market.
Why AI talent is the new oil field
AI today is the new oil. Just as oil in the 20th century created corporate power, AI talent now determines who will own the digital future. Platforms no longer compete only on infrastructure—almost everything can be rented or purchased. The one thing you can’t buy is brains, creativity, and vision. The most powerful GPU clusters, the most expensive data centers, massive volumes of data—without talented specialists, it’s all just “sand.”
Companies that win this race will secure decades of technological and economic leadership. No wonder Facebook, Google, and Microsoft are now competing in “AI team building” as fiercely as they once did for the advertising market.
Prices we’ve never seen before
Back in 2019, $500,000 for an engineer in the US was considered outrageous. By 2025, such numbers stopped being shocking: if a specialist truly drove innovation in the LLM market, worked with the largest language or vision models, participated in leading experiments with self-supervised learning, reinforcement learning, or AIGC (AI-Generated Content), they might be offered anything.
Things have gone so far that 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 land an “engine of growth.”
What is changing in the industry?
1. Facebook (Meta) declares ambitions to become the No. 1 AI company
Meta has long been more than just a social network. Over the past three years, the company has invested tens of billions into the metaverse, AR/VR, generative models, and data-center infrastructure. The bet now is to make Facebook and Instagram the “smartest” platforms: content generation, recommendations, moderation, search, and communications.
Huge resources are aimed at overtaking OpenAI and Google in the quality and accessibility of AI services for everyday users. Inside the company, they say 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 “commodity.” The real value lies in those who invent new algorithms and see how to squeeze more out of hardware and data than competitors do. Meta is poaching not just developers, but architects, scientific leaders, theorists, and people who understand how to scale and deploy AI into real products with audiences of 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 start at $300,000 per year and rapidly grow to a million and beyond. As the market capitalization of Meta, Microsoft, Amazon, and OpenAI grows, the equity component can exceed the “cash-only” salary by multiples.
4. Mass growth of 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 pouring in, the market isn’t saturated, and demand for skills keeps rising.
5. Changing role of universities and open source
Stanford, MIT, Berkeley, and Oxford are becoming not just “talent forges,” but centers of real applied science and startups. The best AI talent now simultaneously works in industry and teaches, or mentors their 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 immediately “snapped up.” Contributor experience in open source is now valued more 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 also opens new paths for regular developers. 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. What’s rewarded is not tenure, but results and open-source reputation
For AI startups, big companies, and investors today, what matters far more than years on a resume is real impact: successful projects, publications, open-source contributions, and solving hard problems. If you want to enter the AI sphere, start building mini-projects, publishing your ideas, and participating 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—first becoming Junior Data Scientists, then Senior, and within 3–4 years reaching management of small teams. “Lifelong education” isn’t a slogan—it’s the market reality. - 4. The return of the “cult of personality” and “geniuses”
Every year, standout individuals matter more and more—both in science and in engineering. Facebook (Meta) deliberately builds its own culture around “AI stars”: freedom of research, the right to make mistakes, budgets for experiments, and—most importantly—the ability to build teams from the best specialists.