A concerning trend is using AI to expand brief thoughts into verbose content, which then forces recipients to use AI to summarize it. This creates a wasteful cycle that amplifies digital noise and exhaustion without adding real value, drowning organizations in synthetic content.

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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.

While AI tools once gave creators an edge, they now risk producing democratized, undifferentiated output. IBM's AI VP, who grew to 200k followers, now uses AI less. The new edge is spending more time on unique human thinking and using AI only for initial ideation, not final writing.

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.

The internet's value stems from an economy of unique human creations. AI-generated content, or "slop," replaces this with low-quality, soulless output, breaking the internet's economic engine. This trend now appears in VC pitches, with founders presenting AI-generated ideas they don't truly understand.

To maintain quality, 6AM City's AI newsletters don't generate content from scratch. Instead, they use "extractive generative" AI to summarize information from existing, verified sources. This minimizes the risk of AI "hallucinations" and factual errors, which are common when AI is asked to expand upon a topic or create net-new content.

A critique from a SaaS entrepreneur outside the AI hype bubble suggests that current tools often just accelerate the creation of corporate fluff, like generating a 50-slide deck for a five-minute meeting. This raises questions about whether AI is creating true productivity gains or just more unnecessary work.

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.

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.

The ease of generating AI summaries is creating low-quality 'slop.' This imposes a hidden productivity cost, as collaborators must waste time clarifying ambiguous or incorrect AI-generated points, derailing work and leading to lengthy, unnecessary corrections.