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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

Adversarial Dogfooding Loop

Force all your work through your own product even when it's the wrong tool, so it becomes the right tool

Adversarial Multi-Model Peer Review

Have rival LLMs review each other's code and fight it out until no issues remain

Agency Over Title: The Force-Multiplier Mindset

Ignore role boundaries — use AI tools to execute your own ideas end to end

AI as Your Personalized Just-In-Time Tutor

Feed AI a curriculum tuned to how you learn, then prove understanding by teaching it back.

Marketing2 steps

Beautifully Simple Pricing

In your early days, price so a customer can repeat it back and it tells a value story.

Strategy2 steps

Benefit = (Volume × Quality) / Time

Measure AI-building impact as experimentation volume times quality, divided by time to launch

Best-Model-First Agent Workflow

Use the most capable model on max effort, start in plan mode, then auto-accept — counterintuitively cheaper.

Be The User Reset (Jobs-to-be-Done)

Zoom out and ask what the user hires your product for — then be that user and ask if you'd even buy what you made.

Be Your Own First Customer: Internal Tools to Product

Turn the ugly tools your team builds to serve customers into the product — most of the value is below the waterline

Break Into AI PM With a Prototype Portfolio

Build a foundation, then ship prototypes that pre-answer the hiring manager's core questions.

Build-and-Bake: Ship the Same Feature Until the Model Catches Up

Prototype ambitious features now, let them sit, and re-release the same shape each time models leap

Builder vs. Information Mover

Diagnose which half of your role AI kills and which half it supercharges.

Build for the Model Six Months Out

Design your AI product for the model that ships in six months, not the one you have today.

Build for Where the Models Are Going

Design products for the capability one to two years out, not today's model ceiling.

Build Infrastructure, Not Features

Ask 'what has to be true so anyone could build this in an hour?' and build that instead.

Build Only at the Magic Intersection

Don't ship what anyone could build off the shelf — build only where model and product uniquely meet.

Build Only Where You Can Be Best (Model Sourcing)

Build in-house only where your unique data or position lets you beat the frontier; otherwise buy

Build-With-The-Tools Assessment

Don't test people on doing work without AI — hand them the tools and score what they can build in an hour

Build Your Own Senior-Engineer Benchmark

Measure AI honestly by scoring new models against real human experts rewriting your actual broken work.

Calories Per Hour: Intensity Over Hours

Get more done per minute instead of working more minutes, then go home.

Cannibalize Your Own Product Every 6-12 Months

Make your current product look silly on a fixed cadence instead of only shipping what users ask for

Strategy5 steps

Center of Gravity: Customer vs Employee vs Investor

Every company has one true center of gravity — know which, and re-engineer incentives to move it deliberately

Champion-Led Agent Adoption

Roll out new AI tooling by letting a few excited early adopters pave the way for the team.

Choose the Hard Path

When choosing between options, pick the harder one because it wins whether or not it works out.

Chop-It-Up AI Collaboration Loop

Don't hand AI one giant spec — specify a little, review a little, repeat in tight loops.

Code Quality Doesn't Equal Product Success

Solve the real problem for real users; architecture and code quality are nearly orthogonal to success

Complement-the-Frontier Model Strategy

Don't rebuild foundation models — train small specialty models that attack their weaknesses in speed, cost, and niche tasks.

Connect-Users-to-Value Journey Staffing

Growth's job is to connect users to your product's value — so staff teams around each stage of the user journey.

Context-First Agent Debugging

When a coding agent won't do what you want, treat it as a missing-context problem, not a model problem.

Context Loading (Additional Information)

Front-load all relevant task information — and put it at the top for caching

Conversation as Ping-Pong (Give-to-Get)

Index toward asking questions, but hit the ball back and forth instead of interrogating or monologuing.

Conway's Law Reorg: Go Functional Before You Go AI

Restructure engineering into one functional org before expecting company-wide AI or technical depth

Credits-for-Sharing Loop

Pay users in product credits to publicly share what they built — turn usage into free distribution.

Crossing the Chasm from Fear to Joy

Reinvent yourself by manufacturing one small moment of building joy, not by studying harder.

Cultivate Agency, Not Skills

When AI hands everyone the skills, agency becomes the only differentiator — and you build it by making things.

Cut Scope, Not Quality

When time is short, strip features down to the core instead of shipping the same feature badly

CV > EV > TV > ME: The Prioritization Hierarchy

Rank every decision by Customer, then Enterprise, then Team, then self — and detect managers who invert it

Decompose the Strategy You Disagree With Into Hypotheses

Break a plan you doubt into assumptions, find the one you reject, and design the smallest test.

Defensible Moats for AI Startups

Four durable places to build in AI where foundation-model labs are least likely to squash you.

Define Success Before You Prompt

The clearer your definition of success and failure, the better the work you get from people or AI.

Demos Not Memos

The first 10% of every project is now free — so build something to react to instead of writing documents.

Design For Tomorrow's Majority, Not The Vocal Minority

Optimize for the far larger future user base, then manage the unhappy few with authentic listening.

Design in the Material

PMs and designers should code — not to ship, but to master the material and truly understand what they're designing.

Detune Precision by Time Horizon

The shorter the horizon, the more detail; keep long-range plans deliberately hazy to avoid false precision

Diagnose With Data, Treat With Design

Data tells you where the problem is; only a creative process tells you how to solve it.

Differentiate Above The Model, Not Around Its Gaps

Build your moat outside the LLM so your product gets better as the models get better.

Dimensionality: Every Strength Is Its Own Weakness

See yourself as infinite dimensions so feedback becomes data, not an identity threat.

Dissolve the Roles: Build Small Builder Teams

Shrink teams and drop role labels so AI-empowered individuals own the whole problem.

Documentation vs Storytelling Safety Framework

Sort every AI-video use case into documenting reality (block) or storytelling (enable)

Dogfood-Driven Realism

Be the end user, use your product intensely daily, and never ship anything that isn't useful to you.

Don't Box the Model In

Give the model tools and a goal, not a rigid workflow — scaffolding gains get wiped out by the next model.

Don't Default to the Chatbot: Choosing the Right AI Interface

Reject the intuitive AI copy; ask what problem your business actually needs solved.

Mindset2 steps

Don't Wait for the Re-Org

If you're waiting for a formal declaration to build differently, you're already too late

Do The Five Fundamentals Harder

Startup success isn't secret knowledge — it's executing the well-known basics far past normal effort.

Influence3 steps

Dress to Signal Effort, Not Expense

Fit beats brand, dress one level up, and ask when you don't know the code.

Drive AI Adoption By Using It Yourself On A Real Problem

Executives using the tool daily on their own real problems beats any top-down mandate or think-piece

Driving AI Adoption Through Hard Constraints

AI transformation moves when you impose hard constraints, sort people into three groups, and fix the slowest part of the system.

Earn The Next Role By Driving Impact In This One

Stop eyeing the next promotion; be judged on impact delivered in the job you already hold.

Elastic-Demand Career Bet

Invest in domains where making people 10x more productive increases demand rather than reducing it

Emotional Journey Design for Content

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

Peak Performance4 steps

Engineering the Creative Peak

Single-shot coffee, a two-hour deadline, and a good night's sleep — dial in the state, not just the effort.

Ensembling (Mixture of Reasoning Experts)

Solve the same problem several ways and take the most common answer

Finance3 steps

Enterprise Land Price Floor (Defendable ACV)

Land enterprise deals at 75K-150K — a cheap land price poisons your expand.

Equally Disappoint Everyone

In your power years, prioritize by spreading disappointment evenly to protect time for reinvention.

Innovation6 steps

Error Analysis: Open Coding to Axial Coding to Count

Turn messy LLM logs into a prioritized list of failures before you write a single test.

Eval Triage: Decide What Actually Deserves an Eval

After counting failures, route each one to a prompt fix, a code check, or an LLM judge — not all three.

Existing-User Retention & Resurrection Priority

As a consumer subscription matures, the biggest lever isn't new users — it's current-user retention and resurrecting the dormant.

Explore & Exploit at the Insight Level

Oscillate between finding the right mountain and climbing it — but do it insight by insight, not just at strategy level.

Exponential Bet Allocation

If AI is your core value, shift the growth portfolio from micro-optimizations to large bets

Exposure Hours

Build taste as a trainable skill by quantifying time spent watching real people use products

Exposure Time for Taste

Deliberately spend more time learning than building to develop the judgment AI can't give you.

Extract the Chain-of-Thought, Not the Recommendation

Treat every advisor as an LLM: mine their reasoning, not their verdict, because their answer is trained on a different corpus.

Fall-On-Your-Face Taste Calibration

Deliberately push AI past its limits in a safe environment to build a gut feel for what it can do.

Fast-Thinking / Slow-Thinking Org Split

Split product org into a weekly-shipping AI group and a deliberate-infrastructure group so both speeds coexist.

Feedback as a Daily Practice: Opt-In, Check Intention, Name the Difficulty

Make feedback frequent and safe by pre-agreeing to it, checking your motive, and admitting it's hard.

Few-Shot Prompting

Show the model examples of what good looks like instead of describing it

Finding High-Leverage AI Ideas

Give AI work a metric, run hackathons, and study what makes AI products feel magical.

Sales3 steps

First-Call Yes-or-No Qualification

On the first call it's yes or no, never maybe — a no is data that saves the relationship.

First-Principles Re-Derivation (Rerun the Decision Tree)

Rebuild any product decision from today's building blocks instead of copying path-dependent solutions.

Flash Tags: Labeling the Intent of Every CEO Message

As you scale, tag every message with its intent so an offhand comment isn't executed as a mandate

Self-Mastery5 steps

Follow People, Not Plans

Choose jobs by who you'll learn from, not by a five-year plan or a financial bet.

Follow the Pull

Bet your career on the thing you enjoy, are good at, and others value — even if it's not the plan.

Marketing5 steps

Four Criteria for Choosing a Distribution Platform (Enter and Exit)

Score a new platform on retention, monetizability, value exchange, and scale — then plan your exit before you enter.

Friction Smells: Signs Your Team Can Move Faster

The tell-tale signals that friction, not capability, is capping your team's speed

From Brick Layer To Architect

Reclaim the 10% of high-leverage architecture work by delegating the 90% of implementation to AI.

Frozen Competence: Where Human Value Survives

Models commoditize yesterday's competence; your value is using that cheap competence to make something new.

Frustration-Log Micro-Tool Ideation

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

Full-Value Free Sampling (Reverse Free Trial)

Make your free product a live, rationed taste of everything paid can do — not a walled-off basic tier.

Gamification's Three Pillars: Core Loop, Metagame, Profile

Durable habit-forming products stand on three legs: a tight core loop, a long-horizon metagame, and an accumulating profile.

Genius-Zone Time Budget

Spend at least 50% of your time on the work you're both great at and love — it's what keeps you showing up.

Give It Your Hardest Task

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

Goal-Talent-Purpose-Process: Managing People and AI With One Playbook

Treat managing agents like managing people: same four levers, different resources.

Innovation2 steps

Golden Samples Over Full Corpus

Curate a small set of gold examples for your AI, don't dump your whole knowledge base on it

Greedy-but-Smart Compute Allocation

Throw hundreds of dollars of inference at high-value problems — the value-to-cost ratio is absurd in your favor.

Growth Team as a Rigor Forcing Function

Hire a growth leader early — the second-order effect is that it exposes everything you haven't measured.

Hand the Toil to the Model First

Delegate the boring, repetitive parts of engineering to agents before anything else, and collapse the path to production.

High-Ceiling Market Test

Attack markets with a high ceiling and easy switching; incumbents win only where there's little left to build.

Hills-and-Valleys: Getting Value From Probabilistic AI Tools

Be patient and explicit, start small, and learn where the model is strong vs weak

Hire a Founder-Cosplayer, Not a Salesperson

For the first enterprise rep, hire someone who can cosplay the founder — vision, not scripts.

Hire an Organism of Strengths

Interview for three universal traits and compose a team of complementary strengths over hiring the most optimal individual stars

Hire by Repelling: The Distinctive Bat Signal

The best talent magnets deliberately turn some people off — clarity on who you're NOT for is the point

Hire Slow, Fire Fast: The Spiky-Hire Executive Playbook

Beat the 50% executive-failure rate by hiring spiky over well-rounded and running real blind references

Hire Yourself First (Build in Public)

Do the job professionally in public before anyone hires you — then the hire is just changing the vehicle.

Hiring for the Extra 20%

Skills are table stakes — screen for the people who'll chase the real outcome, using lateral personality signals

Humor as a Calibrated Mastery Signal

A joke landed right up the line proves you own the room; keep a ranked joke file to do it on demand.

Hunting New Bottlenecks When AI Writes the Code

When AI removes the coding bottleneck, constraints shift up and downstream — go find them.

If You Want to Kill a Plant, Have Two People Water It (The DRI Rule)

Every important cross-functional outcome needs one Directly Responsible Individual with real power

Internal Virality for Alignment

Get a big org aligned by making a prototype go viral inside the company instead of grinding stakeholder meetings

Irrationally Optimistic, Uncompromisingly Realistic

Push hard toward a future you believe in while killing your own hypotheses as data arrives

Job Trials Over Interviews

Interviews don't predict performance; make the interview as close to the real job as possible.

Kind and Candid

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

Knowledge-Work End-State Idea Generation

Pick an area of knowledge work and reason forward to its end state as AI matures — then build for that.

Label the Process Stage, Not the Polish

A production-looking prototype no longer means it's production-ready — say the stage out loud

Latent Demand Product Discovery

Find your next product by watching people misuse your current one for something it wasn't designed to do.

Lean-Into-Organic-Sharing Virality

Don't manufacture virality — instrument where users already share, then make those exact moments 5-10x more delightful.

Self-Mastery5 steps

Lean Into Your Spike

In the AI era, double down on your unfair advantage and stack disciplines to become a unicorn

Learn the Tokens, Not the Depth

In the AI era, master the symbolic vocabulary of a domain rather than its exhaustive depth

Live in the Future, But Not Too Far

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

LLM-Optimized Codebase Architecture

Structure your repo so the AI writes the least code possible — infrastructure absorbs the complexity.

Lower the Courage Required

The scarcest resource is courage, not genius — so design products and decisions to need less of it.

Magic Lenses

Evaluate competing implementation paths by plotting each through the eyes of the advisers you wish you had

Make the Other Mistake (Prompting for Brutal Feedback)

To get honest AI critique, over-correct toward brutal — the model won't actually overshoot.

Many Bets, Charge Early

Maximize the number of fast bets, then force a verdict by asking the customer to pay a lot — now

Sales6 steps

Mastering B2B Negotiations Through Value

Extract full value from every deal via gives-and-gets, value selling, and disciplined negotiation tactics.

Match the Medium to the Point

When implementation is cheap, the skill is choosing document vs prototype for the point you're making

Meeting Armageddon

Once a year, delete every recurring meeting and forbid new ones for two weeks.

Merge With the Unreasonable

Don't try to become a visionary; find one and connect your APIs to theirs.

Milestone-Gated Launch Sequence

Don't spend a dollar on marketing until users start sharing your product unprompted.

Mixed-Initiative Contextual Assistance

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

Model Casting by Personality

Route each task to the model whose 'personality' matches, and split plans to fit

Negotiate From Genuine Indifference

The best position to sell your company is being genuinely fine not selling it.

Nine Nos For Every Yes

Guard product quality with creative restraint — every feature you say yes to is a puppy you must care for forever

NLX: Designing the Natural-Language Interface

Conversation is an interface with real constructs — design it, don't just let the model lead

Obsolete Yourself

Treat every repeatable thing you do as something to replace with software or an agent.

Obviously Good, Then Incremental Correctness

Only make obviously good stuff, ship it in iterations, then reconcile the sprawl back to a naked robotic core.

Optimize for Learning: The Impedance-Match Career Test

Choose roles by where you'll learn most and whether the mission truly matches how humans are wired.

Own-the-Framing Design Partnerships

Design partners guide the build — set the pricing frame up front and filter feedback 80/20.

Pair Programming as a Management Tool

Two people on one computer isn't half the code, it's the fastest path to the right code.

Parallel Draft Divergence

Start one idea 4-5 times in parallel, each with more precision, then pick the obvious winner.

Patient-Then-Floor-It Hiring

Be obsessively patient hiring the founding team; step on the gas the moment demand exceeds what you can handle

Pivot to the Burning Problem

Founders are usually too anchored to their own product vision — index to the customer's real fire instead

Planning in Seasons

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

Platform Betting Strategy by Company Stage

Late-stage companies spread chips across platforms; startups make one focused bet and go all in.

Play-First AI Fluency

Build real AI intuition by playing — invent fun side projects, use everything, and share the artifact not the doc.

Positional vs. Tactical Game

Don't die, then pour your energy into board position — tactics are the drawdown of value you've already earned.

Positive Delusion, Bounded by Learning

Assume everything is possible until proven wrong — but keep learning what's actually possible.

Positive-Sum Dials (Levers You Don't Pull)

Build every monetization lever you can, then refuse to pull them — coordinate on the iterated game instead of defecting for short-term profit.

Practitioner-Sourced Wisdom

The best advice comes from people on the ground doing the thing — not from pontificators.

Prescriptive vs. Framework Metrics

Know whether your metric is a recipe or a lens — and never misuse a recipe

Prime-Then-Parallelize AI Coding Workflow

Front-load the architecture, fan out to agents, then step back and evaluate

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

Product-First AI Prompting

Steer AI builders by describing the end-user experience ambitiously, then iterate like you're coaching a collaborator

Prompt Decomposition

Make the model list the sub-problems first, then solve each before the whole

Strategy5 steps

Prompt Injection Defense Stack

Stop trying to fix injection with prompts and guardrails — mitigate at the model level

Prompt Sets Are the New PRD

Communicate product ideas by building the prototype, not writing the doc — demos before memos

Pull-Based Product-Market Fit

Be stubborn on your thesis but open on its form — chase the customer who is surprisingly easy to sell, not the one you must force

Question the Base Assumption Before Build-or-Buy

Before building OR buying a tool, ask whether the process needs to exist at all

RAG Data Preparation Over Database Tuning

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

Raise Your Happiness Baseline

Big events only move you temporarily — the real lever is lifting the baseline you always return to.

Randomized Tiered Trial for AI Productivity

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

Reading the Invisible Language of the To/CC Line

Recipient order, To vs CC, and reply order all send signals people quietly read.

Communication5 steps

Reframe Metrics to Leadership's Language

Pick two or three metrics that speak the exact word your leaders keep repeating

Ride the AI Value Wave

Treat today's AI capability as the worst it will ever be and expand where it's most efficacious

Ride the Models

Stay valuable in AI by playfully applying each new model to your own work and re-testing what it couldn't do before.

RLAIF Reward Design

Have an expert define success criteria and a rubric once, then let AI reinforce the capability — more scalable than labeling examples

Root-Cause Tooling Post-Mortem

When AI botches something, ask what in its prompt caused it — then patch the tooling

Scheduling by Deference

The one asking does the work: let the busier person set the time, and give real options.

Self-Criticism Loop

Get the model to critique its own answer, then implement its own critique

Sales4 steps

Sell the Alpha (Gap Selling vs Problem Selling)

Sell leaders the opportunity to become a superhero, not a fix for a narrow problem.

Sales3 steps

Services-First Foot-in-the-Door

Sell enterprises a service they already know how to buy, then migrate them to the product.

Simple, Novel, Emotional: The Shareability Formula

Make it simple, make it show something new, make it feel something — then cut the cleverness.

Single-Channel Build-in-Public Bet

Find the one channel that resonates with your exact audience, then double and triple down.

Solve Before Scale

Run in a distinct 'solve' mode before you ever switch to 'scale' mode

Speed-to-Aha Over Best Product

Sometimes the correct product decision hurts the aha moment — cut it and get users to magic faster.

Start Small, Don't Boil the Ocean

Narrow scope to the achievable thing in front of you, then build momentum on top of it

Steering AI to a Non-Obvious Strategy

AI gives predictable strategy when asked lazily — enumerate every input first, then make it argue back

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.

Stickiness Over Moats

Stop chasing barriers competitors can't cross; build accumulating value they can't easily replace.

Strategy2 steps

Stop Churn Before It Happens

To stop churn, acquire customers who won't leave — churn is an acquisition problem, not a save problem.

Strategic Friction

Add friction that helps a user understand why the product is for them; cut friction that doesn't

Strategic Technical Debt as Leverage

Startups should take on technical debt on purpose — it's how you outrun bigger companies

Super Agent With a Forward-Deployed Human

Give the whole company one shared agent, owned by a human who keeps it working — not personal agents for everyone.

Survival Before Thriving

Make asymmetrically positive bets and protect the enterprise — you must survive long enough for timing and insight to arrive

Systems-First Scaling (One to 100)

Once you have product-market fit, go slow to go fast: build the systems before you hyperscale.

Tasks, Not Problems

Delegate to AI agents by handing them scoped, verifiable tasks — never open-ended problems.

Taste as a Trainable Model

Taste is a virtual machine in your head that predicts whether your in-group will like an idea — built by reps with feedback.

The 1,000-Experiments 'What Would Need To Be True' Goal

Set an audacious experiment-volume goal — not to hit it, but to force the conversations that unlock a whole experimentation culture.

The 10-to-1 Input/Output Kill Test

When you pour 10 units of effort in for 1 unit of output, the project has run its course.

The 2004 Red Sox Team-Composition Model

Build your leadership bench mostly from homegrown talent, seasoned with a few marquee outside hires

The 20/80 Willingness-to-Pay Axiom

20% of what you build drives 80% of willingness to pay — and it's usually the easiest 20% to build.

The 4x4 Debugging Framework

Four escalating ways to unstick a broken build, each tried exactly once, ending by teaching the agent.

The Agent Foothold Onboarding

Onboard an AI agent like a new hire: environment first, easy tasks next, then scale.

The AI-as-CTO Persona Project

Cast the AI as an opinionated technical co-founder to kill sycophancy and premature coding

The AI-Document Slop Test

An AI-written document is fine — as long as you can stand behind every line and it took longer to make than to read.

The AI-Fit Accuracy Test

Deploy AI where the human baseline is low-accuracy — not where it's already near-perfect and you need the last 2%

Peak Performance5 steps

The AI Interview-Prep Coach System

Build an AI coach, mine the real question bank, drill weak spots — then mock with humans

The AI Startup Moat: Data Flywheel + Crafted Workflow

Defensibility for AI apps comes from a proprietary data flywheel and a deeply crafted vertical workflow.

The Aligned Binary LLM-as-Judge

Build a one-failure, pass/fail judge and align it to a human with a confusion matrix before you trust it.

Finance4 steps

The Attribution-Autonomy Pricing Matrix

Pick your AI pricing model by plotting value attribution against product autonomy, then climb toward outcome-based.

The Automatable Growth Loop (CACHE)

Break growth experimentation into four evaluable stages an AI can hill-climb, keeping humans on alignment

The Benevolent Dictator Labeling Model

Appoint one trusted domain expert to own eval judgments instead of running it by committee.

Influence3 steps

The Business-Meal Deference Protocol

Order middle-to-last, never the priciest dish, always offer to pay, and keep your tip invisible.

The Cold-Pattern Positivity Flip

Find the moments where users feel bad, and flip the product to reinforce progress instead of failure.

Sales4 steps

The Conversion-Funnel Cold Email

Treat a cold email like a growth funnel: open, then read, then reply — optimize each stage separately

The Curator Leader (Not the Visionary)

The best product leaders don't own the ideas — they build environments where great ideas bubble up and get chosen

The Dehydrated Company Hiring Model

Only hire when a function is underwater, so headcount forces ruthless prioritization

The Design Sprint Scorecard

Break your founding hypothesis into rows and grade each one red/yellow/green after head-to-head customer tests

The DevX Listening Tour

Before you build a tool, walk developers through their yesterday

The Differentiation 2x2 (Escape Loserville)

Plot two customer-facing differentiators you can prove and own the top-right; the other three quadrants are Loserville

The Enterprise AI Adoption Ladder

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

The Escape Hatch Principle

Abstractions should let power users drop to raw control when the model doesn't fit their problem

The Eval ROI Decision

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

The Exposure-Therapy Tool On-Ramp

Ease into coding tools in graduated steps so code stops being terrifying

The Fast-Feedback Flywheel

Fix every piece of user feedback within minutes so people feel heard and give you even more.

Leadership6 steps

The Five PM Archetypes

Hire and compose product teams by five distinct thinking styles, not one generic ideal PM.

Leadership2 steps

The Five Traits to Keep, Automate the Rest

Protect where humans shine — vision, empathy, communication, creativity, judgment — and automate everything else

The Forgotten-Name Recovery Playbook

Tactical moves to survive not remembering names or whether you've met someone before.

The Forward Deployed Engineer Loop

Embed a real engineer inside the customer's building and run a build-show-iterate cycle every single day

Entrepreneurship5 steps

The Foundation Sprint

A 10-hour, 3-phase team sprint that turns a vague startup idea into one testable founding hypothesis

The Founding Hypothesis (Mad Libs Strategy Sentence)

Compress your entire startup strategy into one testable fill-in-the-blank sentence — then go prove it

The Four-Step Distribution Platform Cycle

Every new growth channel opens then closes in the same four predictable steps — learn to see where you are.

Leadership7 steps

The Frictionless Seven-Step DevX Program

A start-anywhere playbook to stand up a developer-experience initiative

The Full-Stack Builder Model

Empower one builder to take an idea to market end-to-end, regardless of role or team

The Full-Stack Role Collapse

In the AI era every role needs a minimum baseline in the adjacent two — deep in one, dangerous in the rest.

Finance3 steps

The Gross-Margin Short-Circuit

Use high gross margin + healthy churn as a fast litmus test for whether a business is truly differentiated

The IKEA Effect for AI Products: Leave Knobs and Levers

Don't automate everything away — give users enough control to feel ownership.

The In-Product Feedback Loop

Stream user reactions straight into your team's consciousness by building feedback into the product itself

The Insight Test

Before founding anything, prove why YOU uniquely hold an insight others don't — and why you'll endure it for a decade

The Internal-Signal Quality Bar

It's done when it makes YOU laugh or tear up — not when someone else approves it.

The Learning-Opportunity Command

Turn every confusing moment into an 80/20 lesson aimed at your current skill level

The Lindy Commitment Test

However long you've sustained something is roughly how much longer you can trust it to last.

The LOCKS Algorithm for Evaluating Founder-CEOs

Five traits that predict whether a founder can actually scale a company: Lovable, Obsessed, Chip, Knowledgeable, Student

Influence3 steps

The Low-Heart-Rate Abundance Posture

Enter high-stakes rooms calm by treating every meeting as one of many, not your one shot.

The Murder Board

Before starting a project, invite smart outsiders whose only job is to tear your two-page plan apart

The Painful-Mistake Growth-Mindset Interview

Screen only for growth mindset by asking for the most painful mistake and what changed because of it.

The Parallel Agent Workflow

Run several AI agents at once and insert yourself only where your expertise actually matters.

The Personal Values Framework for Career Decisions

Write down what you actually care about, then reject anything that violates it, no matter how shiny.

Leadership6 steps

The PM-Engineering Alignment Operating System

Run product and engineering as one leadership unit with clear ownership and async, iterated reviews.

Sales3 steps

The POC as Business Case

Frame every pilot as co-creating an ROI model, charge for it smartly, and never anchor the commercial deal.

The Pod + Product Staff Team Model

Replace the 13-person specialist team with a 6-person generalist pod plus one summoned specialist

The Public and Secret Roadmap

Split your roadmap into what users ask for and what only you can see — the wins come from the second

The Reach Test

Judge whether an AI tool is truly useful by whether you reach for it unprompted each morning.

The Refounding Test

Ask how you'd rebuild AI-native from scratch — then decide whether your legacy asset helps or you should sell.

The Relentless Iteration Loop

Read-through, add, repeat 50-100 times, set aside, return — then hand to editors. That's the secret.

The Say-Do-Said Loop

Say you'll do the thing, say you're doing it, say you did it — to align, adjust, and close the loop.

The Scheduled AI Chief of Staff

Put proactive agents on a schedule to watch your metrics, surface misalignment, and coach you weekly

Productivity7 steps

The Seven-Command Build Loop

Six slash-commands turn vibe-coding into disciplined software delivery for non-coders

The Six-Month Autonomy Razor

If you're still telling a hire what to do six months in, you hired the wrong person.

The Six-Week Review Operating Cadence

Short, frequent check-ins where leaders pair on problems, not catch you slacking, create velocity.

The Skip — Plan the Move After Next

Optimize career decisions for the job two moves out, not the next job.

The SMB-or-Enterprise Binary (Mid-Market Doesn't Exist)

There is no mid-market — decide which of two distinct games you're playing and don't bleed them.

Productivity6 steps

The Source-of-Truth PRD Cascade

Spend a day writing five layered docs so the agent, not you, carries the context on every build.

The SPACE Framework

Pick balanced developer productivity metrics across five dimensions — never rely on one.

The Surgeon Management Model

Spend over half your time making your top 10% feel like a supported surgeon who can't be blocked.

The Talent Stack (Intersection of Uniqueness)

Stack three-to-five things you're uniquely good at and curious about so you know more than everyone else in that tiny space.

The Teammate Onboarding Model for AI Agents

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

The Three Dimensions of an Agent

Score any 'agent' on autonomy, complexity, and natural interaction — each a spectrum

The Three Flavors of AI Product Management

Map any AI PM role into one of three types to target the right skills and job.

The Three-Part AI Product Utility Equation

A useful AI product needs model intelligence, context/memory, and application/UI to all converge.

The Three-Trait Hire Plus Two AI-Era Premiums

Grit, quick-learning, self-awareness get you hired anywhere — curiosity and putting yourself out there keep you relevant

The Tiny Core Principle

Every enduring product has one tiny thing that is a superpower — find it, protect it, and stop bolting on features.

The Toby Tornado (Compressed Change Management)

When you sense a project is wrong, compress the conflict into a short window, kill it, and reconstitute the team as founders of the next version.

The Top-Three Weighted DevX Survey

Force a top-three, weight by frequency, and never ask four questions at once

The Two-Bucket Controversial-Test Triage

Sort every risky experiment into a red-line 'never run' bucket or a 'run it if the return justifies the cringe' bucket

The Two-Engine Profitable Growth Architect

Master market share and wallet share together — equal attention, not equal effort — to avoid the single-engine trap.

The Two-of-Three Inflection Test

Only build a new zero-to-one product when at least two of three inflections line up

The Two-Week Deputization Rule

Under two engineering weeks, the engineer is the PM; over two weeks, the PM owns it

The Weekly Marketable Feature

Every engineer ships one feature per week that a user would pay or show up just for

Three Tenets: Can-Do, High Standards, Intensity

The three cultural tenets Foody credits for the fastest revenue ascent in history

Throwaway Prototyping To Discover The Product

Build disposable prototypes on real data to feel what works before writing any production code.

Sales4 steps

Tier-One Logo First

Chase the Walmarts and Nvidias first — the market leaders are your real early adopters.

Finance3 steps

Token Budgets as a Trust-Proportional Resource

Kill the token-spend leaderboard; cap AI spend like any resource, sized to trust in someone's ROI judgment

Triangulate the Customer's Truth

Don't take what customers say literally — reconstruct their economics and incentives independently, then find the urgent one

Two-Day Work-Test Hire

Bring finalists on-site for two days to do a real end-to-end project — it screens skill and fit at once.

Two-Question New Technology Adoption Test

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

Two-Sided AI Adoption

Pair top-down executive buy-in with a bottom-up tiger team of excited power users.

Unblock the Review Bottleneck

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

Under-Resource to Force Automation

Deliberately under-staff projects and hand out unlimited tokens so people are forced to automate with AI.

Unquantifiable-First Decision-Making

Demote metrics to a support function and let taste, fun, and delight drive — because Goodhart's law means any metric-as-goal degrades.

Update Your Priors: The AI Capability Arbitrage

Re-test what AI can do today and demand more of it — old scar tissue is leaving alpha on the table

Use AI As Leverage On Your PM Time

Assign the model a role, feed it more context than you could read, and iterate the prompt until it works.

Value-Chain Eval

Before applying AI to your business, build a systematic test that measures how well it automates your core value chain

Vibes Before Evals

For a genuinely new AI feature, start with open-ended vibes testing; add evals only once the use-case cluster is clear.

Vision-Strategy-Execution Split (and the Controversy Test)

Vision is the destination, strategy is an opinionated path — and a real strategy must be disagreeable

Waiting vs. Wandering: The IC PM Operating Model

Bring energy, wander into the unknown while others wait, and amplify signal with AI.

What Actually Improves AI Apps

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

When-It-Leaks Communications Pre-Mortem

At scale you can't test quietly — decide the message before you even know you want to launch

When You Eat a Sandwich, Don't Nibble

Deliver hard news and hard change in one decisive move, then deliberately over-correct

Wide Aperture, Then Coalesce

Keep considering many ideas — including ones that look bad — and test cheap versions until the signals converge on one

Win on the Platform, Not the Features

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

Work Alone Together (Note-and-Vote)

Replace group brainstorms with silent individual idea generation, a quiet vote, and one designated decider

Zone Defense for Product Work

Spread taste-makers out to cover the whole field instead of clustering on the same problem