The 3-Layer Framework That Predicts Which Jobs AI Will (and Won't) Replace

Summary

AI is reshaping the economy, creating a bifurcated landscape where digital, contestable markets face increased competition, while physical, local service markets remain protected. Mid-tier digital firms are being squeezed between lean AI-native startups and large firms with distribution advantages, while physical service industries benefit from AI’s efficiency tools. The key to success lies in how businesses invest in AI—whether by getting lean, shifting to high-value judgment work, or enhancing back-office operations. Understanding these dynamics is critical for making strategic AI investments, particularly as we approach 2026.

Takeaways

• 😀 AI is reshaping the economy, creating a bifurcated landscape between contestable digital markets and relationship-heavy physical markets.
• 😀 In digital markets, AI is commoditizing baseline tasks, squeezing out middle-tier businesses that focus on cognitive work.
• 😀 The real strategic risk for mid-tier firms is being caught between lean AI-native startups and large corporations with distribution advantages.
• 😀 Physical service businesses (like plumbing or dentistry) are less vulnerable to AI-driven competition because their markets aren't as contestable.
• 😀 AI is making cognitive work (drafting, coding, analysis) cheaper and more abundant, leading to an explosion in volume rather than a reduction in jobs.
• 😀 Firms can either get lean by reducing overhead or move up the value chain by focusing on judgment, accountability, and quality to stay competitive.
• 😀 AI investments for digital service firms should either focus on improving efficiency or enhancing high-value human judgment and accountability.
• 😀 Local service businesses should focus AI investments on back-office efficiencies, such as scheduling, invoicing, and customer service automation.
• 😀 Startups focusing solely on AI-driven cognitive production are at risk of becoming commodities, and need to differentiate through distribution, compliance, or human-in-the-loop services.
• 😀 Large companies should embrace AI to enhance internal innovation and talent retention, ensuring they adapt to faster-moving competitors without losing their competitive advantages.

Q & A

What is the main issue with the current analysis of AI in business?

The main issue is that most analysis of AI in business is either too abstract, stating that AI will transform everything, or too tactical, focusing on specific applications like using ChatGPT for customer service. What’s missing is a strategic middle layer that addresses how AI impacts competitive dynamics and helps businesses understand where they are vulnerable or protected.

How is AI bifurcating the economy?

AI is bifurcating the economy by affecting different markets in distinct ways. In contestable markets, AI is commoditizing baseline tasks, which intensifies competition and hurts mid-tier businesses. In physical, relationship-heavy markets, AI is reducing overhead but not increasing competitive pressure, often improving margins.

What is the 'middle tier' of businesses, and why are they at risk from AI?

The 'middle tier' consists of businesses like marketing agencies, IT consultancies, and design firms that serve mid-market clients. These businesses are at risk because AI is enabling small, lean teams to produce similar work at lower costs, while larger companies with distribution advantages can offer more comprehensive services that the mid-tier firms cannot match.

How does AI impact industries that rely on physical work, like plumbing or dentistry?

In industries reliant on physical work, such as plumbing or dentistry, AI doesn't intensify competition. Instead, it reduces overhead by automating tasks like scheduling and invoicing, making operations more efficient. AI supports these businesses by streamlining back-office operations without affecting the demand for the physical services they provide.

What is meant by 'tokenizable cognition' in the context of AI?

'Tokenizable cognition' refers to cognitive tasks that can be expressed as text, such as drafting, analyzing, summarizing, coding, or research. These tasks can be handled by language models, and AI is drastically reducing the cost of producing this type of cognitive work, making it nearly free to produce in large quantities.

How does the concept of 'Jevons Paradox' apply to AI in business?

Jevons Paradox suggests that when a technology becomes more efficient (like AI making cognitive work cheaper), the demand for it increases rather than decreases. In the case of AI, instead of reducing the need for cognitive work, businesses are producing far more of it, creating more output, not less.

What are the three layers of work in a business, and how does AI affect each?

The three layers are: 1) Tokenizable cognition (AI-driven tasks like drafting and coding), 2) Judgment and accountability (human decision-making and responsibility for outcomes), and 3) Physical execution (tasks involving the physical world, like installation or caregiving). AI impacts the first layer by reducing costs, but judgment and physical execution still require human involvement.

How can a mid-tier marketing agency survive in the AI-driven economy?

A mid-tier marketing agency can survive by either getting radically lean (cutting overhead and restructuring around a small, AI-powered core team) or by shifting toward higher-value, second-layer work (such as focusing on judgment, accountability, and quality). The worst strategy is to make the existing model more efficient without changing its fundamental structure.

Why is AI beneficial for firms in local, relationship-heavy markets?

In local, relationship-heavy markets, AI is beneficial because it reduces administrative burdens and improves efficiency in back-office tasks (scheduling, invoicing, etc.) without increasing competition. These businesses, like plumbing or dentistry, still rely on human presence and service quality, which AI cannot replace.

What should AI investments look like for large companies with distribution advantages?

For large companies with distribution advantages, AI investments should focus on internal innovation to retain talent and improve operational efficiency. The key risk is not external competition, but the internal challenge of changing operations fast enough to keep up with more agile competitors, particularly in terms of retaining skilled workers.