The problem with bad AI-generated work ('slop') isn't just poor writing. It's that subtle inaccuracies or context loss can derail meetings and create long, energy-wasting debates. This cognitive overload makes it difficult for teams to sense-make and ultimately costs more in human time than it saves.

Using AI to generate content without adding human context simply transfers the intellectual effort to the recipient. This creates rework, confusion, and can damage professional relationships, explaining the low ROI seen in many AI initiatives.

AI's ability to generate ideas and initial drafts for a few dollars removes the high cost of entry for new projects. This "ideation" phase, once proven successful, often justifies hiring human experts for full execution, creating net-new work that was previously unaffordable.

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 can produce scientific claims and codebases thousands of times faster than humans. However, the meticulous work of validating these outputs remains a human task. This growing gap between generation and verification could create a backlog of unproven ideas, slowing true scientific advancement.

Research highlights "work slop": AI output that appears polished but lacks human context. This forces coworkers to spend significant time fixing it, effectively offloading cognitive labor and damaging perceptions of the sender's capability and trustworthiness.

Advanced AI tools like "deep research" models can produce vast amounts of information, like 30-page reports, in minutes. This creates a new productivity paradox: the AI's output capacity far exceeds a human's finite ability to verify sources, apply critical thought, and transform the raw output into authentic, usable insights.

Job seekers use AI to generate resumes en masse, forcing employers to use AI filters to manage the volume. This creates a vicious cycle where more AI is needed to beat the filters, resulting in a "low-hire, low-fire" equilibrium. While activity seems high, actual hiring has stalled, masking a significant economic disruption.

Professionals are using AI to write detailed reports, while their managers use AI to summarize them. This creates a feedback loop where AI generates content for other AIs to consume, with humans acting merely as conduits. This "AI slop" replaces deep thought with inefficient, automated communication.