Anthropic

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

Adversarial Dogfooding Loop

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

Agency Over Title: The Force-Multiplier Mindset

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

AI as Your Learning Engine

Stop only asking AI to do work for you — spend your spare hours making it train you

AI Introspection Rubric

Point an AI agent at a folder of your own notes to surface criteria you hold implicitly and turn them into a rubric.

Ambitious Retry (Pass@N) Tool Use

Get more from AI tools by asking for the ambitious change and fully restarting on failure instead of hammering the same attempt

Bad vs Sad Quality Tiers

Classify every failure as bad (irrecoverable) or sad (recoverable pain) so teams triage quality across many surfaces.

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

Bob the Monster and the Two-Week Rule

Externalize the emotions of change into a monster, let it rage, and only act on what survives two weeks.

Brand as a Promise

A brand isn't a logo — it's a promise you make and keep, worth paying extra for.

Strategy6 steps

Breadth-Then-Depth Platform Strategy

Go wide to discover your niche, then put all your wood behind the few arrows that lift the rest

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 Current Model, Prototype for the Next

Elicit max capability from today's model while pre-building the products the next model will unlock

Build for the Exponential (Skate to Where the Puck Is Going)

Build products for the model that arrives in 6-12 months, betting that today's 20%-working features hit 100%

Build for the Future Model

Design product ideas for the model capability that is coming, not the one you have today

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.

Building the Network Effect Into the Product

For SaaS, ask if the product works better when users tell others — if not, your marketing is doomed.

Building Trust Through Speed

Ship an honest 'research preview' early, then visibly iterate on feedback so speed builds trust instead of eroding it.

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 the Company Only You Could Build

Reject the pivot-and-blitzscale playbook; commit to the one idea that dies without you.

Peak Performance3 steps

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.

CaMeL Permission Pre-Restriction

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

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

Celebrate Adoption, Not Shipping

Replace hours and feature counts with customer-adoption outcomes teams can actually steer toward

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.

Clear-Goal Ambiguity Cut

Because general LLMs can do anything, a sharp key-user + problem + use-case triad is what rules approaches out

Constitutional AI Self-Critique Loop

Align a model to written principles by having it critique and rewrite its own outputs, then train on the fix

Context Loading (Additional Information)

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

Context Over Models: The Data-Management Bet

AI products win on getting good, timely, well-structured data to the model — not on the model itself

Conversation-Intelligence Deal Diagnosis (the Dealbot)

Run an AI agent over every call, email and Slack to find the real reason you win, lose, and stall

Core-Behavior Synthetic Training

Ship an AI feature by naming its 3-4 core behaviors and teaching them with model-generated data

Critique in Public, Build Trust in Private

Invert the management-book rule: debate directly in public, lavish reassurance in private.

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.

CV > EV > TV > ME: The Prioritization Hierarchy

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

Data Is a Compass, Not a GPS

Data disproves the ridiculous — it rarely hands you the answer, so validate findings before you trust them

Decompose-and-Ensemble

Break a broad problem into specific tasks, then solve each with a specialized model in an ensemble.

Deep Human Evaluation Over Leaderboards

Measure real progress with experts who work through the answer, not crowds who vibe for two seconds.

Defensible Moats for AI Startups

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

Deliberate Low-Leverage Leadership

Choose the nitty-gritty tasks 'anyone could do' — because when a senior leader does them, they become high leverage.

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.

Design the Dream Objective Function

You become what you measure — so measure the rich thing you actually want, not the easy proxy.

Determinate vs Indeterminate Optimism (Betting Under Deep Uncertainty)

Founders need one specific plan; allocators need many bets and a discount on their own forecasts

Detune Precision by Time Horizon

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

Differentiate Above The Model, Not Around Its Gaps

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

Differentiation vs Table Stakes

Balance the roadmap between what attracts new customers and what they require to switch at all

Discovery-Led Selling: Ask, Don't Pitch

Great salespeople talk under half the time and answer a question with a question about the question

Marketing4 steps

Distribution Beats a Commodity Product

When products in a category are basically interchangeable, an adequate product with superior distribution wins

Dive-In Career Strategy for a Platform Shift

Facing a disruptive technology, submerge yourself in it and aim to hold at least two of three career pillars

Influence3 steps

Don't Be Stingy With Words

People can't read your mind — make appreciation explicit, and never fake it.

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.

Do The Five Fundamentals Harder

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

Dual-Motion Growth: PLG and Sales That Feed Each Other

Win by having both many customers and lots of revenue — with PLG and sales feeding each other

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.

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.

Escaping the Low-Impact PM Death Spiral

Low-impact work begets low-impact work until layoffs — break the cycle by proactively claiming high-impact work.

Eval-Driven Product Development

Design the test for your AI product alongside the product, then hill-climb the model against it.

Eval-First AI Product Development

Define what 'correct' looks like as evals first; the spec becomes the scorecard, not the build instructions

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.

Exponential Bet Allocation

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

Peak Performance3 steps

Exposure Hours

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

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.

Fast-Thinking / Slow-Thinking Org Split

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

Fear-to-Agency Reframe

When AI change triggers fear, lean in and ask what's within your control — turn it happening to you into happening for you.

Few-Shot Prompting

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

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.

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.

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.

Give Away Your Legos

In a scaling company, repeatedly hand off what you're good at to keep climbing the growing pile.

Go All-In When the Experiment Works

Big companies experiment plenty — they fail by hedging instead of doubling down on what works.

Go-to-Market as a Product

Design the buying journey as a sequence of experiences, and add value at every touch whether or not they buy

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.

Handbook-as-Code

Treat how the company operates as a versioned repo anyone can submit a merge request against

Harder Is Easier (The Figure-It-Out Principle)

Commit to the costly right thing up front, and the trust it earns makes everything else easier.

High Agency, High Accountability

Pair the freedom to solve problems your own way with clear ownership of the hypothesis and the outcome.

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 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

Hiring for the Extra 20%

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

Hoard What You Know How To Do

Keep a searchable backlog of verified, working experiments so agents can recombine them into new solutions.

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

Impact Estimation in the Unit of Your Goal

Prioritize by estimating each option's impact in the exact same unit as your team goal, not an abstract score.

Invest in High Performers by Running Experiments

Stop babysitting low performers; grow your best people by testing theories about their potential.

Irrationally Optimistic, Uncompromisingly Realistic

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

J-Curve vs. Stairs Career Growth

Jump off career cliffs into jobs you're unqualified for; fall for 6-9 months, then climb far beyond the stairs.

Just-in-Time Planning

Shrink long roadmaps to a lightweight monthly priority list and grant explicit permission to kill dead processes.

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 Mining

Watch for people jumping through hoops to make your product do something, then make that the smooth path.

Latent Demand Product Discovery

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

Leader Dogfooding for Product Pulse

Live and breathe your product as a real user (or meet customers) and trust the anecdote over the dashboard.

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

Legibility Framework for Spotting Frontier Ideas

Hunt for illegible ideas — the ones with real energy that nobody can quite articulate yet — and translate them.

LLM as Disconfirmation Engine

Point the model at where your strategy does NOT fit — and reverse-engineer competitors from their public docs

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.

Manager-as-IC Onboarding

New managers ship as individual contributors first — and keep doing it — before and while they manage people.

Manager Visibility via an Always-On Agent

Enlist a standing AI session across all repos and channels to stay on top of 8x throughput and drive conversations.

Many Bets, Charge Early

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

Map Your Product Against What AI Can Do

Start from your product's core job, then classify each part as AI-replace, augment, partial, or not-yet

Marginal-Impact Reasoning Under Tail Risk

Prioritize catastrophic low-probability risks by expected value and by how few people are already working on them

Match the Medium to the Point

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

Meet the Bar and Be Different

Incumbents overshoot average utility — win by meeting the bar while being materially different

Mega Trend vs Hype Cycle — The PhD Test

If you need a PhD to understand it, it's not a mega trend. Don't fight mega trends.

Mission-Above-Product Prioritization

Route every cross-org tradeoff through a single shared mission so decisions are fast and everyone stands behind them

Mission Drive Audit

Prove you're mission-driven by showing you cannot profit except by achieving the mission.

Entrepreneurship5 steps

Mission-Protective Provisions Playbook

Lock in founder and mission protections now, while you still have the leverage to do it.

Model Introspection Harness Repair

When an AI agent misbehaves, ask it why — its explanation reveals the harness gap to fix

Model Maximalism

Build at the edge of what models can barely do — the next model will make it sing.

Multi-Axis Revenue Segmentation

Plot customers on a graph of size plus the attributes that actually drive your revenue, not size alone

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

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.

One-Step-Away Team Goals

Set your team's goal exactly one why-statement or one math operator away from a company goal.

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.

Options-With-A-Recommendation Pushback

Never say yes or no to stakeholders — present options, trade-offs, and one clear recommendation.

Output-Toward-Outcome Measurement

Don't forsake motion for progress — keep asking whether the metric you climb still serves the outcome.

Outside-the-Building Product Management

Spend 80% of your time thinking outside the building and argue every case from the market's point of view

Own-the-Framing Design Partnerships

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

Own the Story — No Packet Loss

The leader is the custodian of the message — deliver it to the front line yourself, never via cascade.

Patient-Then-Floor-It Hiring

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

Pick Hard Problems to Attract the Best Team

Hard, important problems attract the best people — and business is a team sport.

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

Play-First AI Fluency

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

Productivity2 steps

Poor Man's Fine-Tuning (Few-Shot + Role Priming)

Steer a model with in-prompt examples and a role identity instead of a full fine-tune.

Presume Radical Uncertainty (The 1997 Lens)

Treat an emerging platform as if it were 1997 for the internet: assume most of it doesn't work and you can't yet name the winners

Price-Elasticity Three-Response Model

When a technology makes something cheaper, work out which of three demand responses your market will take

Finance3 steps

Price Like a Product (and Unbundle Deliberately)

Align price to where value and cost actually accrue, and kill defaulted freemium and mis-packed SKUs

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

Product-Market-Story Fit

Great product in a great market still fails if the story is wrong or missing — add the third leg

Prompt Decomposition

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

Prompting-as-Prototyping

Validate a product idea by prompting a model in a local browser before building anything

Prompt Injection Defense Stack

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

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

RAG Data Preparation Over Database Tuning

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

Randomized Tiered Trial for AI Productivity

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

Reason About It Like a Human

To design or debug AI behavior, ask what an equivalent human would do in the same situation.

Red/Green TDD for Coding Agents

Force agents to write the test first, watch it fail, then pass — the compressed prompt is just 'red/green TDD'.

Reinforcement Learning Environment Design

Build a fully-fleshed simulated world, inject real chaos, and reward the trajectory, not just the answer.

Resting in Motion

Treat the busy, working state as your normal baseline so heavy long-term work stays sustainable

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.

Innovation6 steps

Right Model for the Right Use Case

Route each AI feature to a model chosen for that job, not one popular LLM stretched over everything

Right to Win via Permission to Play

Before building, ask if it's logical that YOU built it — and whether you have a route to market.

RLAIF Reward Design

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

Roasting Plus High Standards

Let the team feel safe enough to roast you, then apply high standards without fear — safety makes rigor easier.

Seeing the System

Strategic thinking is naming the invisible systems and their self-protecting culture so you can change the rules.

Seek the Counterfactual, Not Confirmation

The competitive edge lives in the data you're trying to prove yourself wrong with, not the data you hoped to see

Self-Criticism Loop

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

Sell the Alpha (Gap Selling vs Problem Selling)

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

Sell to Reduce Risk, Not Increase Upside

Four of five buyers buy to avoid pain or reduce risk — anchor the sale there, not on the art of the possible

Serve the Business, Not the People

Strip emotion out of hard calls by asking what you'd do if the person had no negative reaction.

Services-First Foot-in-the-Door

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

Ship-Fast Operating Model

Lightweight bottoms-up planning, PM-light teams, and iterative deployment in a fast-moving domain.

Ship-in-Research-Preview Speed Loop

Cut idea-to-user time from months to a week by branding launches as previews and pre-wiring the launch chain

Communication5 steps

Short Toes

Engineer feedback so it lands on the work, never the person — the async collaboration protocol

Show Me the Apparatus Test

A value is only real if there's an expensive apparatus that enforces it every time, no exceptions.

Strategy6 steps

Six Rules for Goals and Alignment

Goals are a communication tool: three max, one that wins, intern-legible, painful, single-owned, and followed up.

Spec-as-What-Good-Looks-Like Verification

Check your definition of good into the repo so AI code review can automatically validate work against it.

Spiritual Holding Company (Mission Guardian Selection)

Give the mission its own sovereignty by appointing a renewable guardian that outlives any founder.

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.

Marketing4 steps

Strategic Friction

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

Stratified Design Work

Split your time between supporting engineers' execution and setting a 3-6 month vision, not polishing mocks.

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.

Swinging the Pendulum

Spot an undesirable state, correct it — but know you'll overcorrect, so aim for the middle

Systems-First Scaling (One to 100)

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

Task Loss, Not Job Loss

Your job won't vanish — analyze it as a bundle of tasks and keep swapping the tasks AI absorbs

Tasks, Not Problems

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

Task vs. Job Automation Test

Before predicting a role's automation, ask whether the automatable task IS the job or just one piece of it

Taste and Standards as Meeting Spec

Good taste is knowing what people want just before they do; high standards means relentlessly improving the spec, not hiding behind perfectionism.

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.

Tension at the Center of Strategy

Every great strategy creates 'it might not work' tension — the possibility a customer falls in love with.

The 100%-or-Nothing Automation Rule

An automation that works 95% of the time isn't an automation — push it to 100% or don't rely on it

The 10-Minute PM Test for GTM-Product Partnership

Hire salespeople with enough product depth that engineers can't tell they aren't a PM

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 Access-to-an-Audience Moat

In human data the only durable moat is a trusted, targetable audience — not recruiters or ads

The Agent Foothold Onboarding

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

The AI Deployment Risk Triage

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

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 Security Vendor Due-Diligence Test

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

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.

The Angry-God Containment Lens

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

The Automatable Growth Loop (CACHE)

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

The Barbell Media Diet

Read only the up-to-the-minute and the timeless — distrust everything in the middle

The Benevolent Dictator Labeling Model

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

The Business-Rules Moat: Why 'Forms on Databases' Are Unclonable

B2B SaaS lock-in isn't the UI or data model — it's years of accreted, configured business rules

The Career Bingo Card

Deliberately take adjacent-but-different roles to fill in squares and become 'scribble-shaped'

The CEO Funding Test

Ask 'if I were CEO, would I fully fund my own team?' to expose whether your work is survivable.

Entrepreneurship5 steps

The Company-Inside-a-Company Incubation Playbook

Spin a 0-to-1 business inside a mature company by ring-fencing it completely from the core

Peak Performance5 steps

The Complementary Learning Modes Ladder

Mastery needs many kinds of practice — imitate a master, get preference feedback, get graded, then live it.

The Conversion-Funnel Cold Email

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

The Culture Bank (Todd Park's Deposit Rule)

Treat trustworthiness as an asset: only ever make deposits, never intentionally make a withdrawal.

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 Dark Factory Software Model

Ship production software no human writes or reads — replace review with simulated-user QA swarms.

The Deep Dive PM Interview

Make candidates write real async requirements and defend them — a two-way test for remote PM fit

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 Differentiation 2x2 (Escape Loserville)

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

The Economic Turing Test

Measure transformative AI by whether you'd hire an agent for a real job without knowing it's a machine

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 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.

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 Forces of Switching

Understand any adoption decision as a tug-of-war between attraction, anxiety, habit, and inertia

The Four Strategic Choices

Choose customers, competition, validation, and distribution deliberately — each choice determines your product and your future.

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.

Sales5 steps

The GTM Engineer Agent-Deployment Loop

Turn a sales function into an AI agent by shadowing your best rep, then redeploy the humans up-market

The In-Product Feedback Loop

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

The Lethal Trifecta

Any AI agent that combines three capabilities can be tricked into stealing your data — cut one leg.

The LOCKS Algorithm for Evaluating Founder-CEOs

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

The Middleman-Signal Disintermediation Play

When middlemen and end-customers both come to you directly, that's the signal to build the business yourself

The Murder Board

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

The Nielsen Number: Right-Size Your Research

Interview 7-14 people — fewer teaches too little, more teaches nothing new

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.

Leadership6 steps

The PM-Engineering Alignment Operating System

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

The Pod + Product Staff Team Model

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

The Purple Cow: Building Customer Traction

Make something worth remarking about, and engineer in advance what you want people to say.

The Quality-Volume-Speed Priority Stack

What high-stakes data buyers actually rank: quality first, then volume, then speed

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 Remote-First Operating Stack

Five mechanisms that make an all-remote company across 60+ countries actually work

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

Strategy6 steps

The Six-Factor Company Stack Rank

Timing > market > team > product > brand > distribution — you need all six, ranked.

The Six-Month Autonomy Razor

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

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.

The Subtractive Goal-Shrinking Exercise

Make a bloated set of goals smaller and more concise each round until one specific, urgent number survives.

The Superpowered Individual (Combination-of-Skills Career Moat)

Go deep in one craft, then use AI to add adjacent crafts until you become un-replaceable

The Thin Skeleton Template

Start every project from a minimal template — agents copy its style far better than they follow prose instructions.

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 Transparency Ramp

Default everything to public except three named exceptions, then ramp until it feels uncomfortable

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-Sided Quality Signal

Great curation does two opposite jobs at once: kill the worst, surface the best.

The Two-Week Deputization Rule

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

The Vibe-Coding Blast-Radius Rule

Vibe-code freely when only you get hurt by bugs; stop and take responsibility the moment others could.

The Waterline Model (Snorkel Before You Scuba)

Diagnose team problems from the top down: structure and dynamics, not the people, cause 80% of issues.

Three Designer Hiring Archetypes

Hire for one of three profiles — block-shaped generalist, deep-T specialist, or the crack new grad.

Three-Legged Model Evaluation

Judge a model-harness combo with heavy usage, a trusted five-person taste panel, and ~10 sharp evals

Three-Prototype Ideation

Since prototypes are now free, build a feature three ways and let real use pick the winner.

Three Tenets: Can-Do, High Standards, Intensity

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

Three Worlds Theory of Change

Decide what to actually do about a hard problem by naming the pessimistic, optimistic, and pivotal worlds you might be in

Throwaway Prototyping To Discover The Product

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

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

Two-Question New Technology Adoption Test

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

Under-Resource to Force Automation

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

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

Value-Up-The-Stack Test

To find where the money accrues in a platform shift, test the infrastructure layer for network effects, differentiation, and pricing power

Vertical AI Opportunity Finder

Build where the winning data is locked behind a company or industry's walls — foundation models won't go there.

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

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

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