The AI talent race just became public. In mid-2025, OpenAI CEO Sam Altman claimed that Meta was offering his researchers up to $100 million in total compensation. The claim lit up the industry. Numbers that large signaled a shift in how companies value human capital in artificial intelligence.
Meta is leaning on speed and spending power. According to reports, CEO Mark Zuckerberg is leading an aggressive hiring push, backing billion-dollar acquisitions, and repositioning AI as Meta’s top strategic priority.
OpenAI, on the other hand, is focused on loyalty and mission. Its structure rewards long-term commitment through Profit Participation Units (PPUs), and its leadership promotes a shared purpose built around achieving AGI.
These two approaches are going head-to-head in the same small talent pool. The competition is shaping cultures, changing compensation norms, and pulling researchers from universities, startups, and even each other.
Whoever builds and retains the best team will determine the direction—and the winner—of the AGI race.
When Altman Went Public: The Start of a Narrative War
In June 2025, Sam Altman spoke on the “Uncapped” podcast, hosted by his brother Jack. During the interview, he accused Meta of trying to lure OpenAI researchers with $100 million signing bonuses and even larger annual compensation packages.
Altman said the offers didn’t work. “None of our best people have taken them up,” he claimed. He described Meta’s approach as “crazy” and “insane,” making it clear he viewed the strategy as flawed and aggressive.
The numbers were specific and sharp. This wasn’t vague complaint—it was a message with intent. Altman wanted to shape the narrative.
Strategic Messaging
Altman’s statement functioned on two levels. It warned Meta that its tactics were visible and ineffective. It also signaled strength to OpenAI’s team. By praising loyalty publicly, Altman reinforced internal morale and cast OpenAI as a place defined by principle, not price tags.
He framed OpenAI’s culture as a strategic asset. Through tone and timing, Altman transformed a podcast comment into a company-wide loyalty campaign.
Meta declined to respond. News outlets confirmed there was no statement, no denial, and no clarification.
This silence may have been deliberate. Engaging the claim could have added weight to it. By avoiding a direct reply, Meta may have sidestepped a narrative battle on OpenAI’s terms and avoided keeping the story in the news cycle.

Rising Pay in an Unstable Market
Altman’s claims didn’t appear from nowhere. Over the past few years, top AI salaries have grown faster than any other area in tech.
At leading labs, base salaries now reach $440,000. With bonuses and equity, many researchers earn more than $1 million per year. In 2025, Meta crossed the $2 million mark in offers and was still losing talent to competitors.
Zuckerberg has taken a direct role. He’s made personal outreach to top candidates with offers of $10 million or more. OpenAI has countered with $2 million retention bonuses and equity deals valued above $20 million.
This wave of offers marks a new level. Meta isn’t competing within a market—it’s attempting to reshape it by inventing a higher tier of compensation.
The 10,000x Researcher
The logic behind nine-figure offers starts with a simple belief: a few people can create enormous value. In AI, this belief scales into what some call the “10,000x researcher.”
These individuals do more than write great code. They design new training methods, solve scaling limits, and unlock capabilities others can’t. Their breakthroughs generate billions in value and shift the research frontier forward.
They are few in number and always in demand. Most companies know their names. CEOs are often the ones doing the recruiting.
Is $100 Million Real?
For most researchers, $100 million is out of reach. For a select few—those responsible for foundational breakthroughs—it is feasible.
Building frontier AI models now costs more than $100 million. Labor can represent up to half of that spend. Meta plans to invest over $64 billion this year in AI infrastructure alone. In this context, paying $100 million to secure a key hire is a calculated bet.
The number may be high, but the strategy behind it is clear. Altman’s claim lands because it reflects a deeper reality: top-tier AI talent now commands a price tag once reserved for entire companies.
Meta has identified a gap in its AGI progress and is working to close it with capital. CEO Mark Zuckerberg is personally leading the charge. His focus is on a new team called “superintelligence,” which has now overtaken even the metaverse in strategic importance.
Llama 4 disappointed. Competitors, including OpenAI and DeepSeek, are advancing faster. Zuckerberg wants Meta to not just catch up but overtake. His method is clear: recruit fast, build fast, and bet big.
Buying Talent Through Acqui-Hires
One major move was Meta’s $14.3 billion investment in Scale AI. The deal brought in CEO Alexandr Wang to lead Meta’s AGI efforts and gave Meta control of a company critical to the AI infrastructure stack.
The deal also gave Meta insight into Scale’s customers—many of whom are Meta’s competitors. But this came with risk. Google, Scale’s biggest customer, reportedly began pulling back after the announcement.
The Danger of a Cash-First Culture
Signing bonuses work quickly—but their effect may not last. Teams built on short-term financial incentives can lose cohesion. When contracts expire, loyalty may vanish with them.
Meta is already facing high turnover. A 2024 report showed a 4.3% attrition rate in its AI division. Retention over two years stood at 64%, compared to 67% at OpenAI and 80% at Anthropic.
The cultural gap is widening. If Meta’s high-stakes hires fail to deliver, the strategy could break under its own cost.
What the AI Talent War Means for Everyone Else

Meta and OpenAI dominate headlines, but the talent war spans the entire AI ecosystem. Other players are using distinct approaches to stay in the game.
Google still commands respect through DeepMind. However, it is losing talent steadily—to both Meta and OpenAI. DeepMind’s researchers are viewed as prime targets.
Anthropic is rising with a different playbook. It avoids large pay packages. Instead, it offers clarity of purpose and a stable, researcher-driven culture. Its retention rate—around 80%—is the best in the field.
The Flow of Talent
Data reveals a steady migration. Google lost 5.4% of its AI team in a single year. OpenAI and Anthropic gained. These shifts reflect more than competition—they reveal how attractive a workplace truly is over time.
Long-term retention is now a strategic indicator. Meta’s two-year rate is 64%. Anthropic’s is 80%. These numbers shape how multi-year research efforts succeed—or stall.
The Acqui-Hire Strategy
When direct hiring fails, companies are now acquiring entire teams. This strategy—known as the “acqui-hire”—has become widespread.
Besides Meta’s latest deal for Scale AI, Microsoft has absorbed Inflection AI in 2024 in a $650 million move. Similarly, Google secured Character.AI’s co-founders through a $2 billion agreement.
These deals remove friction. They allow buyers to gain tight-knit teams quickly, without fighting over individuals.
For companies chasing AGI, buying alignment is often faster than building it.
Verdict Summary: In the AGI Race, Talent Is the Core Advantage
Artificial General Intelligence will be built by a rare class of researchers with extreme leverage. These individuals are now the focal point of competition. They define the pace, direction, and limits of progress.
Meta is applying capital pressure. It is investing at scale, acquiring teams, and reorganizing itself to move faster. The approach is built on access, speed, and volume.
OpenAI is applying cultural pressure. It uses equity, shared mission, and internal belief to retain and motivate. The structure is slower but more stable—designed for continuity and conviction.
Anthropic offers a quiet model. It emphasizes safety, focus, and internal alignment. Its high retention rate reflects the strength of that design.
As pressure rises, large tech firms are bleeding talent. Startups are being absorbed before they mature. Universities are losing faculty to industry. Talent is consolidating in a few labs with the resources and vision to compete at the frontier.
Evaluating AI companies now requires a shift. Product demos and funding rounds tell part of the story. Retention rates, internal culture, and leadership stability tell the rest.