Early AI models simply followed instructions literally. But consider this scenario:
A user asks an AI to "edit my email to sound more professional." The AI rewrites it completely, changing the meaning entirely.
Technically correct. Practically wrong.
As AI becomes more capable and takes more consequential actions, it needs to:
Think of it as a constitution for AI behavior — a public document describing:
| What it does | Why it matters |
|---|---|
| Sets behavioral boundaries | Prevents harmful outputs |
| Defines values | Guides judgment in new situations |
| Establishes a chain of command | Clarifies whose instructions take priority |
The updated Spec organizes everything around five values. Think of them as a pyramid:
[FAITHFULNESS TO HUMAN INTENT]
↓ foundation
┌──────────────────────────────────────┐
│ Harm Honesty Agency Care │
│ Minimization & Transparency │
└──────────────────────────────────────┘
(The Foundation)
What it means: Understanding what people truly want, not just what they literally say.
Three layers of intent to consider:
| Layer | Who | Role |
|---|---|---|
| OpenAI | Writes the Spec | Sets absolute boundaries |
| Developers | Build applications | Direct use within boundaries |
| Users | End users | Direct work within developer constraints |
Key insight: Faithfulness goes beyond literal instruction-following.
Example from the article: When helping someone compare job offers, the AI might recall that the user previously mentioned valuing "predictable hours" — and apply that preference while making the assumption visible and easy to correct.
Why this matters:
What it means: Respecting firm safety boundaries and accounting for foreseeable consequences.
Two types of limits:
FIRM BOUNDARIES SOFT DEFAULTS
(Non-negotiable) (Can be adjusted)
↓ ↓
Never help create Tone, detail level,
weapons, CSAM, etc. content warnings, etc.
Key principle: Consider not just immediate intentions but also longer-term interests and effects on others.
What it means: Being truthful about:
Why it matters: Users can only evaluate AI reasoning if they can see it clearly.
Practical implication: The AI should not pretend to be certain when it isn't, and should not hide its reasoning process.
What it means: Strengthening people's ability to:
The key tension to understand:
❌ AI doing everything FOR you
vs.
✅ AI expanding what YOU can do
The goal is empowerment, not dependency.
What it means: Attending to the circumstances of those affected by AI actions.
This includes:
The values are not a checklist — they interact and sometimes create tension.
| Value | What it demands |
|---|---|
| Faithfulness | Help the user — they asked for this |
| Harm Minimization | Consider societal effects of one-sided persuasion |
| Honesty | Don't present one-sided content as balanced truth |
| Human Agency | Don't quietly add a disclaimer the user didn't want |
| Care | Consider who might read this essay |
Resolution: The Spec provides guidance on navigating these trade-offs — not eliminating them, but reasoning through them systematically.
| New Section | What it addresses |
|---|---|
| Faithfulness to human intent | Values are primary; instructions are signals of those values |
| Updated chain of command | Clarifies conflict resolution between OpenAI/developers/users |
| Consider context carefully | How memories and conversation history inform understanding |
| Supporting human agency | Explicit defaults for empowering users |
| Social norms guidance | How to follow relevant professional/social expectations |
| Side effects guidance | Controlling and communicating unintended consequences |
| Retired Section | Why |
|---|---|
| Detailed style guidance | Style should adapt to individual/cultural preferences, not be fixed |
Key insight on style: The AI should not infer style preferences from what it thinks users want to hear. Users must explicitly request a particular style.
Old approach: New approach:
"Follow rules" → "Understand WHY the rules exist"
↓ ↓
Fails in new Applies values to
situations unfamiliar situations
The article describes this as doing the right things for the right reasons.
"The Model Spec describes the behavior we are working toward; our models do not yet fully reflect it."
This is important: The Spec is an aspirational document — a target, not a description of current reality.
┌─────────────────────────────────────────────────────┐
│ MODEL SPEC CORE FRAMEWORK │
├─────────────────────────────────────────────────────┤
│ │
│ FOUNDATION: Faithfulness to Human Intent │
│ → Understand values, not just instructions │
│ │
│ SUPPORTING VALUES: │
│ 1. Harm Minimization → Safety boundaries │
│ 2. Honesty → Transparent reasoning │
│ 3. Human Agency → Empower, don't replace │
│ 4. Care → Context & consequences │
│ │
│ STRUCTURE: │
│ OpenAI → Developers → Users (chain of command) │
│ │
│ GOAL: Right things for the right reasons │
└─────────────────────────────────────────────────────┘
Test your understanding:
Why is "faithfulness to human intent" the foundation rather than just one of five equal values?
What is the difference between a firm boundary and a default in the Model Spec?
Why did OpenAI retire detailed style guidance? What replaced it?
How does the chain of command (OpenAI → Developer → User) resolve conflicts?
What does it mean for AI to do the right things for the right reasons — and why does the distinction matter?
Bottom line: The updated Model Spec moves AI governance from rule-following to value understanding — equipping AI to handle novel situations by reasoning from principles rather than looking up answers in a rulebook.