If AGI is concentrated in a few US companies, other nations could lose their economic sovereignty. When American AGI can produce goods far cheaper than local human labor, economies like the UK's could collapse. They would become economically dependent "client states," reliant on American technology for almost all production, with wealth accruing to Silicon Valley.

AI systems from companies like Meta and OpenAI rely on a vast, unseen workforce of data labelers in developing nations. These communities perform the crucial but low-paid labor that powers modern AI, yet they are often the most marginalized and least likely to benefit from the technology they help build.

A powerful mental model for the future of work is a three-step pipeline. If a job can be done remotely in a high-cost country, it can be offshored to a low-cost one. Once offshored and process-driven, it becomes a prime target for AI automation. This positions remote work as a transitional phase, not an endpoint.

OpenAI is launching initiatives to certify millions of workers for an AI-driven economy. However, their core mission is to build artificial general intelligence (AGI) designed to outperform humans, creating a paradox where they are both the cause of and a proposed solution to job displacement.

The narrative of AI destroying jobs misses a key point: AI allows companies to 'hire software for a dollar' for tasks that were never economical to assign to humans. This will unlock new services and expand the economy, creating demand in areas that previously didn't exist.

Professor Russell argues that the dominant approach to AI, "imitation learning," is flawed for creating beneficial tools. By training models to replicate human verbal and written behavior as closely as possible, we are inherently building replacements for human jobs, not power tools to enhance human capabilities. This design choice sets up an inevitable economic conflict.

The enormous market caps of leading AI companies can only be justified by finding trillions of dollars in efficiencies. This translates directly into a required labor destruction of roughly 10 million jobs, or 12.5% of the vulnerable workforce, suggesting market turmoil or mass unemployment is inevitable.

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.

The real inflection point for widespread job displacement will be when businesses decide to hire an AI agent over a human for a full-time role. Current job losses are from human efficiency gains, not agent-based replacement, which is a critical distinction for future workforce planning.