OpenAI's new "General Manager" structure organizes the company into product-line P&Ls like Enterprise and Ads. This "big techification" is designed to improve commercial execution but clashes with the original AGI-focused mission, risking demotivation and attrition among top researchers who joined for science, not to work in an ads org.

Meta's decision to cut 600 jobs, including tenured researchers, from its Fundamental AI Research (FAIR) lab reflects a strategic pivot. The stated goal to "clean up organizational bloat" and "develop AI products more rapidly" shows that big tech is prioritizing immediate product development over long-term, foundational research.

An influx of Meta alumni, now 20% of staff, is causing internal friction. A 'move fast' focus on user growth metrics is clashing with the original research-oriented culture that prioritized product quality over pure engagement, as exemplified by former CTO Mira Murati's reported reaction to growth-focused memos.

Sam Altman's evolving stance on ads, from a "failure state" to an opportunity, suggests a shift driven by investors to commercialize ChatGPT. This pivot, marked by key hires like Fiji Simo, was likely necessary to overcome internal resistance from the company's research-focused origins.

Anthropic projects profitability by 2028, while OpenAI plans to lose over $100 billion by 2030. This reveals two divergent philosophies: Anthropic is building a sustainable enterprise business, perhaps hedging against an "AI winter," while OpenAI is pursuing a high-risk, capital-intensive path to AGI.

Departures of senior safety staff from top AI labs highlight a growing internal tension. Employees cite concerns that the pressure to commercialize products and launch features like ads is eroding the original focus on safety and responsible development.

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.

When tech giants release low-ambition AI products, it damages their ability to recruit top talent who are drawn to mission-driven projects. This forces companies to significantly increase signing bonuses to compensate for the less inspiring work, turning a product launch misstep into a costly talent acquisition challenge.

For elite AI researchers who are already wealthy, extravagant salaries are less compelling than a company's mission. Many job changes are driven by misalignments in values or a lack of faith in leadership, not by higher paychecks.