Building Ladders

Building Ladders is both a practice and a philosophy: creating tools and systems that extend human potential by helping people climb to expertise that would otherwise take years to reach.

Ladders don’t replace experts—they make expertise usable.

The Core Principle

For decades, computing has followed one rule: humans adapt to computers. We memorize shortcuts, learn syntax, internalize file systems, attend training sessions.

Building Ladders flips this rule. Instead of humans adapting to machines, machines adapt to humans—speaking your language, understanding your intent, and translating complex domains into terms you already understand.

Why Ladders, Not Bridges?

Bridges are static. Ladders adapt.

A bridge spans a gap, but you still have to cross it under your own power. Documentation, training manuals, tutorials—these are bridges. They’re useful, but they don’t change shape when you’re missing a rung.

A ladder lifts you. Each rung is reachable from the last, and the ladder adjusts as you climb.

AI-powered ladders can:

• Meet you at any level — Whatever understanding you’re starting from
• Adapt to how you think — Using metaphors and mental models from domains you already know
• Enable incremental climbing — Each step accessible from the previous
• Open high-friction domains — Work that normally requires years of training

Building Ladders in Practice

Consider what it means to encode expertise as climbable rungs.

A tax accountant knows hundreds of rules, exceptions, and judgment calls accumulated over years. Traditional software forces you to learn those rules yourself, or hire someone who already knows them. A ladder-based system captures that expertise in a form you can climb—guiding you through decisions, explaining the reasoning, escalating when human judgment is required.

Technically, these rungs are skills—reusable procedures that bundle expert knowledge with guardrails, tests, and documentation. When you invoke a skill, it executes as a tool call: structured inputs, validated outputs, logged decisions.

In other words: the expertise becomes callable. (For more on how this works, see The Core Four Framework.)

The key moves:

Start with human intent. What is this person actually trying to accomplish? What do they already know? What mental models are they working with?

Make each rung reachable. Break complex domains into incremental steps. Each step should be understandable from where someone currently stands.

Extend capability with guardrails. Compliance workflows that guide non-experts and know when to escalate. Analytics that surface insights without requiring data science training. Natural language interfaces backed by tests and reviewable outputs.

Ladders, Not Walls

A ladder that “just does things” becomes a wall.

Most “helpful” technology still follows extractive logic:

• Dependency — Tools designed so you can’t function without them
• Lock-in — Platforms that hold your data hostage, making leaving expensive
• Addiction — Systems that capture attention rather than serve it
• Opacity — Black boxes that do things for you without teaching you anything

Ladder-based systems reject this. They’re designed around a different question: What would it mean if the goal was your independence?

• Educate while empowering — You should understand more after using the system, not less
• Universal procedures — Skills that transfer across platforms, knowledge you own
• Data sovereignty — Your data is yours; the system works with it, not captures it
• Build toward independence — The goal is that you eventually don’t need the ladder
• Show their work — Reasoning, assumptions, and sources visible when needed
• Stay editable — Users can inspect, modify, and override outputs

The Business Reality

Most software tools are bridges—they assume you already speak their language. When you implement a new CRM, ERP, or automation platform, you’re expected to:

• Attend training sessions
• Learn new interfaces
• Adapt your workflows to the system’s logic
• Hire specialists who already understand the tool

This is expensive, slow, and excludes people who could contribute but don’t have the prerequisite knowledge.

Ladder-based systems invert the burden. They translate complex capability into the language your team already speaks, let experts focus on judgment calls instead of repeatable questions, and shorten time-to-competence—without pretending expertise isn’t real.

A Design Choice

The question isn’t whether we can build ladders—AI makes this more possible than ever. The question is whether we choose to.

Every system we design is a choice: build walls that exclude and constrain, or build ladders that help people climb.