Paying billions for talent via acquihires or massive compensation packages is a logical business decision in the AI era. When a company is spending tens of billions on CapEx, securing the handful of elite engineers who can maximize that investment's ROI is a justifiable and necessary expense.

The 30-40% pay premium for AI PMs isn't just because "AI is hot." It's rooted in the scarcity of their specialized skillset, similar to how analytics PMs with statistics backgrounds are paid more. Companies are paying for demonstrated experience with AI tooling and technical fluency, which is rare.

In the AI arms race, a $10 billion investment from a trillion-dollar company is seen as table stakes. This sum is framed as the cost to secure a handful of top engineers, highlighting the massive decoupling of capital from traditional value perception in the tech industry.

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.

Despite general AI hype, the demand for AI Product Managers (AIPMs) is real, reflected in median compensation packages that are now competitive with top-tier software engineering roles in major tech hubs like the Bay Area.

The 'cracked engineer' archetype is a direct response to AI's growing capabilities. As AI automates the work of average engineers, the value of human engineers shifts to exceptional tasks. Companies now prioritize hiring these highly productive superstars who can supervise multiple AI instances, as AI itself can handle the rest.

The traditional tech compensation hierarchy has inverted. Top AI engineers at companies like Meta are receiving four-year liquid stock packages worth a billion dollars, surpassing the illiquid, long-term carry of even the most successful venture capitalists. This marks a significant shift in the most lucrative roles in tech.

After reportedly turning down a $1.5B offer from Meta to stay at his startup Thinking Machines, Andrew Tulloch was allegedly lured back with a $3.5B package. This demonstrates the hyper-inflated and rapidly escalating cost of acquiring top-tier AI talent, where even principled "missionaries" have a mercenary price.

The CEO of ElevenLabs recounts a negotiation where a research candidate wanted to maximize their cash compensation over three years. Their rationale: they believed AGI would arrive within that timeframe, rendering their own highly specialized job—and potentially all human jobs—obsolete.