The intense talent war in AI is hyper-concentrated. All major labs are competing for the same cohort of roughly 150-200 globally-known, elite researchers who are seen as capable of making fundamental breakthroughs, creating an extremely competitive and visible talent market.

In the hyper-competitive AI talent market, companies like OpenAI are dropping the standard one-year vesting cliff. With equity packages worth millions, top candidates are unwilling to risk getting nothing if they leave before 12 months, forcing a shift in compensation norms.

Multi-million dollar salaries for top AI researchers seem absurd, but they may be underpaid. These individuals aren't just employees; they are capital allocators. A single architectural decision can tie up or waste months of capacity on billion-dollar AI clusters, making their judgment incredibly valuable.

OpenAI previously had highly restrictive exit agreements that could claw back an employee's vested equity if they refused to sign a non-disparagement clause. This practice highlights how companies can use financial leverage to silence former employees, a tactic that became particularly significant during the CEO ousting controversy.

The "golden era" of big tech AI labs publishing open research is over. As firms realize the immense value of their proprietary models and talent, they are becoming as secretive as trading firms. The culture is shifting toward protecting IP, with top AI researchers even discussing non-competes, once a hallmark of finance.

Perplexity's talent strategy bypasses the hyper-competitive market for AI researchers who build foundational models. Instead, it focuses on recruiting "AI application engineers" who excel at implementing existing models. This approach allows startups to build valuable products without engaging in the exorbitant salary wars for pre-training specialists.

The frenzied competition for the few thousand elite AI scientists has created a culture of constant job-hopping for higher pay, akin to a sports transfer season. This instability is slowing down major scientific progress, as significant breakthroughs require dedicated teams working together for extended periods, a rarity in the current environment.

In rapidly evolving fields like AI, pre-existing experience can be a liability. The highest performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.

Despite Meta offering nine-figure bonuses to retain top AI employees, its chief AI scientist is leaving to launch his own startup. This proves that in a hyper-competitive field like AI, the potential upside and autonomy of being a founder can be more compelling than even the most extravagant corporate retention packages.