Dan Shipper's AI-forward company, Every, doubled in size over the past year. He argues that automation is not a replacement for humans; every agent and automated system requires human oversight, management, and maintenance, thus creating more work and new roles.

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.

Despite AI's power, even researchers at frontier labs report a median productivity boost of 2x. They emphasize that their complex AI systems would quickly drop to near-zero productivity if the human were completely removed, highlighting the continued necessity of "human salt" for meaningful work.

If AI were perfect, it would simply replace tasks. Because it is imperfect and requires nuanced interaction, it creates demand for skilled professionals who can prompt, verify, and creatively apply it. This turns AI's limitations into a tool that requires and rewards human proficiency.

AI's primary impact is not wholesale human replacement but rather collapsing the middle of the value pyramid by automating routine knowledge work. The value of human workers will shift to higher-level judgment and strategic oversight, where AI can structure options and simulate outcomes, but humans retain final say due to liability concerns.

AI is creating a grim feedback loop where displaced white-collar workers are finding employment in data annotation. In these roles, they are paid to train the very AI systems that eliminated their previous, higher-skilled careers, perpetuating the cycle of automation.

Previously predicting significant job loss, OpenAI's Sam Altman now believes the "jobs apocalypse" is unlikely. He admits his initial intuitions were off, recognizing that the human elements of work, organizational friction, and the value of human interaction are harder for AI to replace than anticipated.

The current trend of replacing domestic engineering talent with AI parallels the offshoring wave of the early 2000s. Just as offshoring led to unforeseen communication and quality issues that brought clients back, using AI for complex projects creates similar problems, ultimately forcing companies to seek senior human engineers for rigor and experience.

AI requires a "Human Sandwich" workflow, with a human framing the task and evaluating the output. Since AI generates competence based on past data, it floods the market with "good enough" work. This paradoxically increases the demand for high-level human experts who can provide the differentiation and value that AI cannot.