How to Position Yourself for the AI Future (Before It's Obvious)

How to Position Yourself for the AI Future (Before It's Obvious)

Timing is everything in a phase transition. The people who got wealthy from the internet weren't the ones who understood it best in 2010. They were the ones who positioned correctly in 1995 — before the transition was obvious to the majority.

We are in the equivalent of 1995 for AI. The transition is real and the trajectory is clear, but most people are still treating it as a novelty rather than a structural shift. That window — between "visible to early movers" and "obvious to everyone" — is where positioning happens.

Here's how to use it.

Understand the Actual Transition

Most people misread the AI transition because they focus on the wrong level.

The surface level: AI tools that make you faster at existing tasks. ChatGPT writes faster, Midjourney generates faster, AI coding assistants code faster.

The actual level: The marginal cost of cognitive labor is approaching zero. Tasks that required expensive human time — writing, analysis, code, design, research — are becoming essentially free to produce at scale.

When something becomes essentially free, the economy around it reorganizes completely. What used to be scarce (cognitive output) becomes abundant. What used to be abundant (human attention to direct it) becomes the scarce resource.

Positioning correctly means understanding this reorganization, not just using faster tools.

The Positioning Framework: Three Moves

Move 1: Get to the leverage layer, not the commodity layer

In every technological transition, the same pattern emerges. Early adopters who stay at the commodity layer (producing whatever the technology makes cheap) eventually compete with everyone else doing the same thing at zero margin.

The people who win are at the leverage layer — they're using the technology to coordinate, direct, or own systems, not just to produce outputs.

In the AI transition:

• Commodity layer: Using AI to produce content, code, designs, analysis faster
• Leverage layer: Directing systems of AI agents to produce output at scale; owning the distribution or curation that selects what matters from the abundance of AI output; building the workflow infrastructure that others plug into

Practically: If you're using AI to write faster, you're at the commodity layer. If you're running a content operation with AI agents that produce at 100x your previous volume while you focus on positioning and distribution — you're at the leverage layer.

Move 2: Build moats that AI can't replicate

The AI transition creates brutal commoditization in some areas and massive scarcity premium in others. The scarce resources in an abundant-AI world:

• Deep domain expertise: AI produces generic outputs. The person who knows, deeply, how a specific industry actually works — its unstated rules, the people who matter, the real decision factors — has knowledge AI can't replicate from public training data
• Authentic trust networks: Real relationships built over years of demonstrated reliability. AI can simulate relationship; it can't replace the accumulated trust of decades
• Taste and editorial judgment: The ability to select what matters from an ocean of AI-generated content. Curation at high quality is rarer than production was before AI
• Distribution: Access to an audience that genuinely pays attention. In an AI-saturated information environment, existing attention is worth more, not less

Build toward these. Protect what you have.

Move 3: Accumulate knowledge of AI systems themselves

The deepest long-term positioning is understanding how these systems work — not at a coding level necessarily, but at the level of:

• What they're good at, what they're bad at, where they fail
• How to architect workflows using multiple AI systems
• How to evaluate AI output quality in your domain
• How to think about AI capability trajectories and what becomes possible next

This isn't "learn to code." It's developing the kind of AI fluency that makes you the person others rely on to figure out how to use these tools in specific contexts.

Sector-Specific Positioning

Different fields have different timelines and different leverage points:

Knowledge workers (writers, analysts, consultants, lawyers): The commodity layer is being automated now. Positioning requires either specializing so deeply in a domain that generic AI can't replace your specific expertise, or moving to the leverage layer (directing AI systems for clients rather than doing tasks for clients).

Creators (artists, musicians, designers): Generalist creative work is becoming commodity. Deep original voice, specific aesthetic, authentic human perspective — these hold value. Community built around genuine connection holds value. Positioning: protect the authentic, leverage AI for scale and production.

Builders (engineers, product managers): AI accelerates building dramatically, which means speed to value matters more. The person who can direct AI to build 10x faster, while maintaining judgment about what should be built and why — this is genuinely scarce.

Business owners: Automation of operational functions (customer service, content, admin, analysis) creates significant leverage for anyone who adopts quickly. Early adopters get productivity gains that compound into competitive position. Positioning: move down the cost curve faster than competitors.

The Skills Worth Building Now

Given the transition timeline, the skills worth investing in over the next 3–5 years:

1. Prompt engineering and AI workflow design — directing AI systems to produce quality output at scale
2. AI system evaluation — knowing when AI output is good enough and when it's wrong
3. Agent orchestration — building and managing multi-agent AI systems
4. Domain depth in areas AI can't easily replicate — specialization that compounds into genuine expertise
5. Distribution building — growing an audience or network before the information environment becomes more saturated

What's not worth building: generic skills that AI already does well. Speed-writing, generic design, standard analysis, basic research — these are commodity skills in an AI world.

The Timing Question

The window for positioning in a phase transition is real but not unlimited. In the internet transition, the window for early advantage was roughly 1995–2003. After that, the advantages of incumbency made it much harder for latecomers to compete in most internet-native categories.

We are somewhere in the AI equivalent of 1997–1999. The transition is visible and accelerating. The infrastructure is being built. The commodity layer is filling up. The leverage layer is still largely open.

The question isn't whether to position. It's how quickly you can move from understanding to action.

Key Takeaways

• We are in the equivalent of 1995 for AI — the window between "visible to early movers" and "obvious to everyone"
• The actual transition is not faster tools; it's the collapse of marginal cost of cognitive labor
• Three positioning moves: get to the leverage layer, build AI-resistant moats (deep expertise, authentic trust, taste, distribution), and accumulate AI systems fluency
• Sector timing varies but the general direction is: move down the cost curve in operations, move up the value chain in the work that remains
• The 3–5 year skills worth building: AI workflow design, system evaluation, agent orchestration, domain depth, distribution
• The positioning window is real but finite — the window for leverage-layer positioning is open now

Part of the Abundance OS framework — the definitive guide to exponential AI, energy, and the collapse of scarcity.