GPT-5

Adaptive Evaluation Over Static Benchmarks

Measure AI robustness with attackers that learn, not with a frozen dataset of yesterday's attacks

Adapt the Model, Don't Build One

Post-training is the new pre-training — steer an off-the-shelf model to your outcome instead of pre-training your own

Barrels-and-Ammunition Team Design

Staff each team from the gap, not from a fixed PM/EM/designer template

CaMeL Permission Pre-Restriction

Grant an agent only the permissions its stated task needs, decided before it runs

Productivity5 steps

Context Is All You Need Prompting

Treat the model as a brilliant stranger with zero context, and supply what a colleague would already know.

Continuous Calibration, Continuous Development (CCCD)

A CI/CD-style loop for non-deterministic AI: scope, evaluate, deploy, then calibrate against surprises.

Productivity4 steps

Draft-First LLM Augmentation

Never ask the model to do your job — write your version first, then have it improve it.

Communication4 steps

Emotional Journey Design for Content

Content is predicting reader reactions: hook them, pace the emotion, make people likable.

Communication4 steps

Evals as Articulating Success

An eval is just a clear spec of ideal behavior — the shared language of AI product work

Evals-Plus-Production-Monitoring Dual Feedback Loop

Reject the false dichotomy: evals catch what you know, production monitoring catches what you don't.

Fake the AI Before You Build It

Never train a model for an MVP — prototype the AI's output and test demand first.

Frustration-Log Micro-Tool Ideation

Beat the idea crisis: log a week of frustrations, then build tiny AI tools to kill them.

Productivity3 steps

Give It Your Hardest Task

Evaluate a serious AI tool on your gnarliest real problem, not a dumbed-down toy.

High Agency, High Urgency Hiring Filter

Hire for two traits only — people who see a problem and go, and people who go now.

Communication3 steps

Kind and Candid

Reframe candor as an act of kindness so you actually deliver the hard message.

Live in the Future, But Not Too Far

Hold the far-future vision, but land with users where they already work today.

Mindset3 steps

Measure in Hundreds

If your unit of measurement is one hundred attempts, five failures means you have effectively tried zero times.

Mixed-Initiative Contextual Assistance

Surface AI help at the moment it's relevant instead of interrupting with notifications.

Planning in Seasons

Replace rigid roadmaps with secular 'seasons', loose quarterly OKRs, and deliberate slack

Innovation5 steps

Product as Organism: the metabolic loop

Treat an AI product as a living system that ingests signals, tunes on rewards, and improves with every interaction

Innovation5 steps

RAG Data Preparation Over Database Tuning

The biggest RAG quality wins come from preparing data for retrieval, not picking a database.

Leadership3 steps

Randomized Tiered Trial for AI Productivity

Measure whether AI tools help by running a randomized trial split across performance tiers.

Strategy5 steps

Run Toward the Hard Use Cases

Don't disable high-stakes uses to avoid downside — engineer them to be great

Innovation5 steps

Ship-to-Learn: The Emergent-Product Loop

When product properties are emergent, launching is how you discover them

Step-Wise Eval Design for Multi-Step AI Apps

Don't evaluate agents end-to-end; put an eval on every step until you hit coverage.

Influence4 steps

The Adjacent-Precedent De-Risk Pitch

Win leadership buy-in for a big AI bet by anchoring it to a past bet that already worked.

The Agency-Control Autonomy Ladder

Ship AI in graduated versions, trading human control for machine agency only as trust is earned.

The AI Deployment Risk Triage

Classify any AI deployment into one of three risk tiers before spending a dollar on defense

Self-Mastery5 steps

The AI PM Upskilling Path

Learn the fundamentals, shadow a research scientist an hour a week, and build one model end to end.

Strategy5 steps

The AI Security Vendor Due-Diligence Test

Five questions that expose whether an AI guardrail vendor is selling real protection or theater

Leadership3 steps

The AI Success Triangle

Successful AI adoption is a people problem first: great leaders, good culture, and technical progress.

Mindset3 steps

The Angry-God Containment Lens

Assume the AI is a malicious agent trying to hurt you, then engineer so it structurally can't

The Better Tool, Same Problems Lens

Every model leap gets normalised within months — build for the boring future, not the euphoric one.

The Constrained-Resource Headcount Test

When the bottleneck isn't people, each new hire is a net productivity loss unless they uplevel everyone.

The Enterprise AI Adoption Ladder

Three sequential stages that separate companies winning with AI from those spinning their wheels

The Eval ROI Decision

Build evals where failure is catastrophic or you must win; vibe-check the rest.

The Four Challenges of AI Product Management

Uncertainty, pivots, data scarcity and a broken promo path — the four taxes of the AI PM role.

The Maximally Accelerated Question

A forcing question that separates critical path from what can wait

The Model Launch Bar

With probabilistic products, the PM — not the scientist — decides what accuracy is good enough to ship.

Strategy5 steps

The Platform Encroachment Test

Build where the platform's mission says it will never go — the general layer is not yours to own.

The Proxy Goal Ladder

Every metric you chase is a proxy — climb the ladder to the mission before you optimise it.

The Shiny Object Trap (Problem-First AI)

A regular PM ships the right product; an AI PM solves the right problem.

Productivity4 steps

The Teammate Onboarding Model for AI Agents

Adopt a coding agent the way you'd onboard a new intern — pair first, then delegate.

Entrepreneurship6 steps

Treat Your Course Like a Product

Hypothesise the audience, interview them, iterate the ICP, and run three weeks — not one.

Productivity5 steps

Two-Mode Prioritization for AI Products

Prioritize backward from model magic AND forward from customer needs

Two-Question New Technology Adoption Test

Before adopting any new AI tool, ask: how big is the gain, and how painful is the exit?

Unblock the Review Bottleneck

The limiting factor on AI productivity is human review speed — engineer the agent to validate its own work.

What Actually Improves AI Apps

Stop chasing AI news and vector DBs; the real levers are users, data, and prompts.

Win on the Platform, Not the Features

Durable products win on invisible infrastructure — reliability, reach, privacy — not on the feature list