AI Was Supposed to Save Time. Why Am I Busier?

The Automation Paradox: Why AI Increases Demand for Experts

A prevailing assumption about artificial intelligence is that by making skilled human expertise cheap and universally accessible, it will inevitably replace human experts. High-profile figures across industries have voiced fears that agentic AI could wipe out large swaths of white-collar jobs, from entry-level to highly skilled positions.

However, practical application at the frontier of AI integration reveals a paradox: automating expertise actually increases the demand for experts.

While AI fundamentally changes the nature of knowledge work, deploying autonomous agents does not eliminate the need for human involvement. Instead, it shifts the value of human labor toward orchestration, refinement, and system management.

The Allocation Economy: Working Like a Manager

The future of interacting with AI systems closely mirrors human managerial skills. As AI agents take on more discrete tasks, the most valuable human skills become:

• Delegation: Knowing what tasks to hand off and to which agent.
• Task Breakdown: Splitting complex objectives into manageable, discrete units for different agents to process.
• Intervention: Knowing when to step back and when to micromanage the output.

The Two Primary Models of AI-Augmented Work

In practical, day-to-day business operations, work with AI currently takes two main shapes:

1. Asynchronous Delegation

This is the model most commonly predicted by early AI discourse. It involves treating AI agents like team members within communication platforms (like Slack).

• Humans assign tasks to specific bots tailored for distinct functions (e.g., editorial assistants, marketing researchers, or operational managers).
• These agents handle tasks ranging from brand research and A/B testing analysis to drafting client proposals and presentation decks.

2. Agent Orchestration Systems

This is an emerging and highly impactful paradigm where agent software acts as an operating system for daily work.

• Humans work within a graphical user interface (GUI) where each chat spins off a dedicated agent on the local machine.
• These agents have deep access to the user’s computer, local files, connected applications, and a built-in browser.
• Work is done in a continuous loop of AI-human collaboration—ranging from managing an inbox and analyzing financial P&Ls to deep software development.

The Necessity of Human Collaboration

A common misconception is that once an AI agent is set up, it functions perfectly autonomously. In reality, the further an agent operates from human correction, the worse its output becomes.

Effective AI systems require constant human intervention. For example, a business running entirely on an automated AI consulting agent still relies on a senior AI engineer whose sole job is to monitor the agent, identify areas where it falls short, and continuously optimize its performance. The AI provides massive leverage, but it does not function in a vacuum; true power is unlocked when humans and agents are tightly aligned in a collaborative loop.

The Economics of “Cheap Competence”

To understand why experts are still needed, it is necessary to examine what AI actually automates.

Language models are trained on vast amounts of data, which serves as the “visible residue of human competence.” Because of this, AI makes yesterday’s rare and expensive skills (e.g., writing code for a pull request) incredibly cheap and accessible to anyone.

When competence becomes cheap, adoption skyrockets. Employees across all departments—from operations to customer service—suddenly gain the ability to generate code, design assets, and draft complex documents.

The Resulting Glut and the Concept of “Slop”

This universal access creates a massive glut of generic, automated work. If a user lacks underlying expertise and relies entirely on default, low-effort AI prompts, the output will be mediocre.

This generic output is increasingly referred to as “slop.” Slop is not defined by specific grammatical quirks or formatting errors; rather, it is the distinct, recognizable pattern of repetitive, default generation that occurs when a single tool is used across a variety of circumstances without human nuance.

Why Cheap Competence Increases the Demand for Difference

When everything looks the same, the market demand for differentiation, quality, and high-level execution skyrockets. This is where experts become indispensable.

When anyone can generate a baseline draft, experts are required to:

1. Refine and Validate: Take a generic AI draft and transform it into a functional, production-ready asset.
2. Build Operational Systems: Create the structures, repository rules, and social contracts required to manage and integrate the massive influx of AI-generated work.
3. Elevate the Floor of Innovation: Experts use AI-generated baseline competence as a foundation to achieve feats that were previously impossible, such as a single individual successfully running an entire software product on their own.

The Zeno’s Paradox of AI

The relationship between human experts and AI can be compared to Zeno’s Paradox of Achilles and the tortoise. In the paradox, Achilles can never catch the significantly slower tortoise because every time he closes the gap, the tortoise has moved a little further ahead.

In the modern knowledge economy, humans are the tortoise starting with a massive head start, and AI is Achilles rapidly closing the gap. However, as AI hoovers up “yesterday’s competence,” human experts utilize that very AI to move their own capabilities further ahead. As long as the AI is economically viable, fundamentally open-ended, and continually running, human roles will dramatically evolve, but human value will not be eliminated.

Actionable Takeaway: Ride the Models

For knowledge workers, the most effective strategy is to “ride the models.” As AI models improve, they package more “cheap human competence” that can be wielded as a superpower. By learning to use these tools deeply, curious and adaptable professionals can multiply their output, driving the continuous demand for their distinctly human expertise.

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Added: 2026-05-25