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The tech industry now has two distinct classes of labor. In AI-native companies like Anthropic, elite researchers have immense power, dictating strategy and leaving eight-figure stock packages. In contrast, at traditional tech companies like Block, non-AI employees have become fungible, with management holding unprecedented leverage to enact deep cuts.

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The constant shuffling of key figures between OpenAI, Anthropic, and Google highlights that the most valuable asset in the AI race is a small group of elite researchers. These individuals can easily switch allegiances for better pay or projects, creating immense instability for even the most well-funded companies.

Leaders from OpenAI, Google, and Anthropic are openly and consistently predicting profound disruption to the labor market from AI. This view, once an outlier, has become the conventional wisdom in the tech C-suite, signaling a major shift in expectations for the near-term future of work.

Jack Dorsey's decision to cut Block's workforce by 40% is being framed as the first major "AI cut." The stated rationale wasn't poor performance but the increased efficiency from AI tools enabling smaller teams. This move signals to the tech industry that drastic restructuring is now on the table to adapt to new AI capabilities.

AI is driving a K-shaped economy. At the macro level, the AI sector booms while others decline. At the corporate level, AI stocks soar past others. At the individual level, a skills gap is widening between those who can leverage AI and those who can't.

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.

The productivity gains from individual AI use will become so significant that a wide performance gap will emerge in the workplace. The most talented employees will become hyper-productive and will refuse to work for organizations that don't support these new workflows, leading to a significant talent drain.

Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.

AI is exacerbating labor inequality. While the top 1% of highly-skilled workers have more opportunity than ever, the other 99% face a grim reality of competing against both elite talent and increasingly capable AI, leading to career instability.

AI disproportionately benefits top performers, who use it to amplify their output significantly. This creates a widening skills and productivity gap, leading to workplace tension as "A-players" can increasingly perform tasks previously done by their less-motivated colleagues, which could cause resentment and organizational challenges.

Horowitz explains the sky-high valuations for AI researchers by noting their skills are not teachable in universities. This expertise is a unique, "alchemistic" craft learned only by building large models inside a few key companies, creating a small, highly sought-after, and non-academically produced talent pool.

A Stark Dichotomy in Tech: AI Researchers Wield Ultimate Power While Traditional Tech Staff are Fungible | RiffOn