The Andrew Chen Founder’s Playbook – AI Prompts to replicate Growth Strategies, Tactics and Systems for Startups and Scaleups
Whether you're launching a marketplace startup, building loops, or scaling product-led growth, this newsletter covers growth tactics, mental models, ai prompts, and frameworks inspired by Andrew Chen.
I’ve been reading Andrew Chen’s writing for over six years (since 2019) ever since I started building my first marketplace.
Back then, I was following the usual growth hacks that everyone tried. Then a friend shared Andrew’s blog.
It felt like someone had switched on a light. His frameworks didn’t just explain growth — they explained why growth worked.
Since then, I’ve read every article, podcast, and case study of his like a playbook.
I mapped his cold start stages to my own product phases, rewrote his loops into my GTM docs, and even created GPT prompts to personalize his lens to every growth challenge I face.
I’ve turned his thinking into a Founder’s Operating System, with tactics you can use, prompts you can run, and questions that make your next decision 10× clearer.
We are gonna cover the following “AI Prompts” below, apart from the strategies and tactics that Chen has shared so far…
AI Prompt #1: BLITZSCALE VS. SLOW BUILD DECISION
AI Prompt #2 - Design 3 Retention Experiments Using Andrew Chen’s Playbook
AI Prompt #3 — Having a Chen like Mental Model for Growth Dynamics
AI Prompt #4 - B2B SaaS Product-Led Growth Loop Design
AI Prompt #5 - Funnel vs. Flywheel + GTM Shift
AI Prompt #6 - Build a 2-Sided Marketplace (Hard Side, Incentives, Onboarding)
AI Prompt #7 - Define Your Product’s Atomic Network
AI Prompt #8 - Select the Right North Star Metric for a SaaS Product
If you’re not a subscriber, here’s what you missed earlier:
The Complete Lenny Rachitsky Playbook : Prompts, Growth Frameworks, and Strategies - Part 2 of 2
The Lenny Rachitsky Playbook : Prompts, Growth Frameworks, and Strategies - Part 1 of 2
5M$ ARR with 6 people team - How Adam Robinson bootstrapped RB2B - Part 1 of 2
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Career Journey & Context
Andrew Chen’s Path:
Startup founder (failed to find product/market fit) → growth hacker → Uber’s head of Rider Growth (drove exponential growth from 1 billion to 2 billion rides in 6 months) → **General Partner at Andreessen Horowitz (a16z)**.
He started as a growth blogger in 2007. His favourite topics were “how to distribute” and “grow consumer products”.
He started advising and angel investing in startups like Dropbox, AngelList, and Gusto. And organized conferences on growth.
In 2015, he joined Uber during its hyper-growth era to apply his playbook at a massive scale.
By 2018, a16z’s Ben Horowitz dubbed Chen “the world’s leading expert at consumer and bottom-up SaaS product distribution” and tapped him to join a16z as General Partner to coach the next generation of founders.
Two Sides of Coin
At Uber he embraced a mindset “rapid expansion at all costs, like Reid Hoffman’s model” to outrun competitors.
Conversely, as a VC he often advocates steady, compounding growth loops. This is more like “grow better, not just bigger”.
“Not all phases call for blitzscaling. Sometimes founders must deliberately go slower to nail retention and product fit. The key is knowing when to sprint and when to pace yourself for endurance.”
AI Prompt #1: BLITZSCALE VS. SLOW BUILD DECISION
PURPOSE:
This prompt helps founders and growth leaders evaluate whether their startup is ready to blitzscale, or if they should slow down to optimize retention and strengthen their product core first.
Inspired by Andrew Chen’s The Cold Start Problem and blitzscaling principles, the prompt guides teams to make a phase-aware, evidence-based growth decision, grounded in:
Retention curve behavior
Loop efficiency
CAC-to-LTV ratio
Network density
Team execution velocity
Use this when:
Preparing for fundraising or scale decisions
Retention is underperforming but acquisition is growing
Growth feels unscalable, or unclear
You need a system-first decision
PREP QUESTIONS — What You Should Be Ready to Answer
These questions make the prompt effective by giving it real data/context. Founders can answer quantitatively or qualitatively.
Company Stage
What stage are you in? - Pre-PMF / Early PMF / Post-PMF / Scaling (Series A+)
Are you seeing strong signs of product-market fit? e.g., rising retention curve, usage spikes, positive feedback loops
Retention & Loop Behavior
What does your current retention curve look like? e.g., Day 1 = 40%, Day 30 = 12%, flattens at 8%
Do users return organically after activation?
Do you have any product loops running? e.g., viral invites, content loops, usage-triggered invites
Are your users part of a networked experience or atomic unit? e.g., a workspace, shared doc, chat group, async feedback loop
CAC & LTV
What’s your customer acquisition cost (CAC)?
What’s your estimated or observed LTV (lifetime value)?
Is CAC increasing as you scale?
Team Velocity
How quickly can your team ship experiments or iterate on product?
Do you have tracking and analytics in place to measure loop and funnel performance?
WHAT THE PROMPT WILL DELIVER
Once inputs are provided, the AI will return:
Strategic Recommendation
Should you blitzscale or pause to optimize retention?
Clear rationale tied to metrics, behavior, and team stage
Evaluation Summary
Snapshot analysis of:
Retention curve shape
Loop strength
Network effects / atomic unit formation
CAC vs. LTV trend
Execution velocity
Tactical Plan Based on Phase
If Blitzscale:
3–5 rapid-growth moves (e.g., scale proven loops, expand atomic networks, increase CAC budget intelligently)
If Optimize Retention:
3–5 retention-centric improvements (e.g., speed up TTV, rebuild onboarding, increase reinvestment points)
Key Metrics to Monitor
CAC, LTV, retention lift, loop throughput, activation-to-invite rate
Phase-specific goals (e.g., flattening retention curve, reducing CAC payback period)
USE CASES
Strategic offsites
Board and investor discussions
Product/Growth team realignment
PLG or GTM inflection points
AI-assisted prioritization for founders
Prompt:
Act as a growth strategist, startup founder, product-led operator, and VC — channeling Andrew Chen’s approach from The Cold Start Problem. Help a founder or leadership team decide whether to:
1. Blitzscale — prioritize top-of-funnel growth, speed, and market capture2. Optimize retention — focus on product loops, activation, and lifetime value
Use data and mindset frameworks grounded in network effects, retention curves, CAC/LTV, loop dynamics, and team velocity.
# Inputs:
Ask the Founder for These Inputs:
1. Stage of company e.g., Pre-product-market fit, early PMF, post-PMF, Series A+
2. Current Retention Behavior- What does your retention curve look like (Day 1, 7, 30)?- Does it flatten, decay, or rise over time?- Any signals of habit formation or churn patterns?
3. Current CAC vs. LTV- Is CAC rising or falling?- Is LTV modeled based on early payback period, churn, or repeat use?
4. Product & Loop Dynamics- What type of product is it? (e.g., PLG SaaS, marketplace, social app)- Do any growth loops exist (viral, content, sales-driven, usage-based)?- What is the loop’s health — is it compounding, stalled, or forming?
5. Team Bandwidth & Infrastructure- How fast can your team ship experiments or close feedback loops?- Do you have reliable instrumentation for growth and retention?
# Steps:
## Step 1:
Analyze Tradeoffs Using Andrew Chen’s Framework
Help the founder evaluate based on:
- Retention curve shape: Can the product hold users long enough to monetize or grow virally?
- Loop maturity: Are any self-sustaining loops driving compounding growth?
- Network density: Are atomic networks being formed (e.g., engaged teams, active workspaces)?
- CAC/LTV dynamics: Is there efficient paid growth, or are you fueling a leaky bucket?
- Team velocity: Is your org structured to run rapid experiments at scale?
## Step 2:
Recommend One of Two Paths
### Path A: Blitzscale
If retention is strong enough, loops are forming, and CAC/LTV supports faster acquisition...
Provide:
- Why blitzscaling makes sense at this moment
- 3–5 next steps to support high-velocity scaling e.g., Double down on best-performing channels, hire for growth ops, expand atomic networks
- Key risks (e.g., growing faster than your onboarding/infra can support)
### Path B: Optimize Retention
If retention curve decays or flattens, CAC is inefficient, or loops aren’t compounding yet...
Provide:
- Why it’s better to slow down and optimize the core experience
- 3–5 high-impact retention-focused actions e.g., Tighten activation funnel, build reinvestment paths, deepen single-player value
- Metrics to monitor before re-attempting scale
## Step 3:
Bonus: Deliver a Phase-Specific Strategy
- For pre-PMF: prioritize user understanding, activation clarity, and early loops
- For early PMF: run retention tuning, measure loop formation speed
- For post-PMF: run growth vs. retention stress tests, monitor CAC vs. net revenue retention
- For funded/scale stage: balance high-velocity acquisition with LTV proof and infra capacity
# Output:
Final output should include:
- Recommendation: Blitzscale or Optimize
- Supporting evidence (based on user journey, loops, CAC/LTV, team dynamics)
- Tactical roadmap (team focus, experiments, metrics)
- Strategic rationale using Andrew Chen’s growth mindsetSignature Wins & Business Outcomes
At Uber, Chen led the Rider Growth product team during a period when the company scaled from 100 million signups to a global mobility platform, spending nearly $1 billion annually on growth efforts.
This investment paid off in a stunning inflection point – Uber took 5½ years to reach its first billion trips, but hit the second billion just 6 months later.
Chen attributes this hypergrowth to building efficient growth loops and local network effects in each city (Uber became a collection of “hundreds of hyperlocal marketplaces” with self-reinforcing demand and supply).
AI Prompt #2 - Design 3 Retention Experiments Using Andrew Chen’s Playbook
PURPOSE
This prompt helps founders and product teams design three rapid, high-signal retention experiments using principles from Andrew Chen’s growth playbook.
It’s designed for teams trying to:
Diagnose what’s driving or blocking retention
Validate hypotheses quickly before building heavy features
Explore activation gaps, habit triggers, and behavioral loops
This is ideal for:
B2B or B2C SaaS
Consumer apps
Marketplaces, mobile products, or anything with usage-based retention
PREP QUESTIONS FOR FOUNDERS
You need to prepare answers to the following before using the prompt:
1. Product Basics
What does your product do?
Who is your primary user?
2. Retention Context
What’s your current retention curve? (e.g., Day 1, 7, 30)
Where do users typically drop off?
What’s your working retention hypothesis?
(e.g., “Users churn because they don’t experience value on day one.”)
3. Core Behavior
What is your product’s core action (the “aha” moment)?
What % of new users complete that action?
What user behavior signals retention?
4. Testable Persona
Who are you testing with? (e.g., power users, template users, new signups, reactivated users)
WHAT THE PROMPT WILL DELIVER
Once you run the prompt with the above answers, it will return:
3 Rapid Retention Experiments
Each with:
Hypothesis + design
Success metric
Expected outcome
Risk and mitigation plan
Use Cases
Use this toolkit to:
Power internal growth meetings
Run founder <> advisor experiments
Guide PMs during roadmap reviews
Design onboarding or lifecycle tests
Prompt:
Act as a hybrid of Andrew Chen, a growth product strategist, and a retention-obsessed PM. Help a founder or team rapidly test retention hypotheses by designing 3 high-signal experiments, inspired by Andrew Chen’s retention playbook.
The goal is to use fast, learnable tests to discover what drives user stickiness — not to optimize blindly.
# Input:
Ask the Founder/Team for:
1. Product description (1–2 sentences) Example: “An AI writing tool for marketing teams”
2. Retention challenge or hypothesis (if known) Example: “Users don’t return after first content draft”
3. Core action the product relies on for long-term engagement Example: “Publishing an article” or “Creating a campaign brief”
4. User cohort or persona to test on Example: “Freelance marketers who sign up via templates”
# Task:
Design 3 Rapid Retention Experiments
For each, include:
1. Experiment Name & Hypothesis
- A short, descriptive name
- What you’re testing (e.g., “Will email nudges based on incomplete projects improve return visits?”)
2. Metric to Track
- The specific behavioral metric tied to retention Example: % of users who return within 7 days, % who complete core action twice
3. Expected Signal
- What success looks like
- Directional thresholds (e.g., “10% lift in Day 7 retention for test group”)
4. Risk & Mitigation
- What might go wrong (e.g., cannibalization, false signals, bad user experience)
- How to limit damage, false positives, or user trust erosion
# Rules:
1. Each experiment must be lightweight and executable within 7 days
2. Tests must align with one of the following retention levers:
- Improving activation
- Encouraging habit formation
- Increasing return triggers
Use examples where appropriate (e.g., Uber’s early driver reactivation, Dropbox’s shared folder reminders). Ensure the final experiments are tactical, insight-rich, and low-risk.Chen’s Advise to All Founders and Growth Leaders (primarily for product-led growth) …
Instead of only tracking top-line user counts, look at cohort retention curves (are users sticking around?) and viral loop throughput (do active users bring in more users?).
“If your users don’t stick, they can’t invite others” – meaning a large initial spike is worthless if those users churn.
Founders should also identify their product’s potential flywheel: how can one cohort of customers drive the next cohort? Optimize that loop.
Finally, be cautious with blitzscale-at-all-costs: ensure you’re also building a moat (through user delight, brand, data, etc.) so that fast followers can’t easily steal your base. In practice, this might mean investing in customer success and community even while you rapidly acquire users. Win sustainably, not just quickly.
AI Prompt #3 — Having a Chen like Mental Model for Growth Dynamics
PURPOSE
This prompt helps founders and growth leaders create a mental model for understanding their growth dynamics like Andrew Chen.
The model turns complex, abstract behaviors (like retention curves, growth strategies, growth loops, CAC, virality, or network effects) into a clear metaphor — enabling:
Strategic alignment
Smarter prioritization
Faster decision-making
Clearer communication across teams and investors
Think of it like inventing your own version of:
“Escape velocity”
“Flywheel”
“Network magnetism”
“Leaky bucket”
“Gravity well”
PREP QUESTIONS FOR FOUNDERS
You need to reflect on the following questions before using the prompt.
1. Product Overview
What is your product?
Who is your primary user or customer?
2. Growth Challenge
What is your biggest growth bottleneck right now?
e.g., activation, retention, loop decay, acquisition plateauWhat stage are you in?
Pre-PMF / Early PMF / Post-PMF / Scaling
3. Growth Motion
Do you rely on:
Viral/referral loops?
Content or UGC loops?
Sales or usage-based expansion?
Paid acquisition?
What part of the user journey is strongest?
e.g., acquisition, time-to-value, collaboration
What part is weakest?
4. Behavior & Systems
Does your product have:
Single-player or multiplayer dynamics?
Network effects or atomic units?
Growth loops or funnels?
Are behaviors compounding or decaying?
WHAT THE PROMPT WILL DELIVER
Once the inputs are provided, the GPT will generate:
1. A Custom Mental Model
Name + metaphor (e.g., “network gravity”, “loop leakage”, “friction slope”)
Visualizable and intuitive for the team
2. Strategy Application
How to use this model to:
Focus team energy
Choose what to build or fix
Decide when to scale or optimize
3. Measurement Signals
What metrics indicate movement within the model?
e.g., activation rate, time-to-invite, loop throughput, churn velocity
What to track as a leading/lagging indicator
4. Real-World Inspiration (Optional)
Reference models from Uber, Slack, Dropbox, or Clubhouse if relevant
How similar metaphors were used in high-growth startups to guide decision-making
USE CASES
Founder <> advisor 1:1s
Strategy offsites
Early-stage GTM alignment
Pre-seed/seed pitch decks (for visualizing growth systems)
Internal onboarding for product or growth teams
Prompt:
Act as a growth strategist, systems thinker, startup founder, and Andrew Chen–style mental model builder. Help a founder understand their product’s growth dynamics through a new, original mental model — one that turns abstract growth challenges into a metaphor or visual framework that can guide strategic decision-making.
The mental model should:
- Simplify complex growth mechanics (loops, churn, CAC, virality, retention)
- Use a metaphor that’s intuitive, sticky, and actionable (e.g., “gravity well,” “flywheel decay,” “network magnetism”)
- Align with the product’s current growth reality and challenges
# Input:
Ask the Founder/Team for These Inputs:
1. What is your product, and who is it for? e.g., “A project management tool for agency teams”
2. What’s your biggest growth challenge right now? e.g., “Users drop off after first project” or “High acquisition, low retention”
3. Do you have any existing growth loops or user behaviors? e.g., content creation, collaboration, invite flows, API usage
4. What stage is your company in? e.g., Pre-PMF, early PMF, scaling post-Series A
# Steps:
## Step 1: Create a New Mental Model
Generate a visual metaphor or mental model to describe their growth mechanics.
Examples (for inspiration):
- “Gravity well” — hard to get users in, but easier to retain once inside
- “Flywheel decay” — loops that used to work are now slowing down due to friction or lack of reinvestment
- “Magnetized network” — the more active nodes, the stronger the pull on new users
Create a unique metaphor that reflects:
- The product’s core motion (e.g., loop vs. funnel)
- The bottlenecks or acceleration points
- The level of user dependency or network density
## Step 2: Apply the Mental Model to Decision-Making
Once the metaphor is created, show how it applies to:
1. Strategic Focus
What should the team double down on?
What’s the one constraint that limits compounding?
2. Team-Level Tactics
Product: What to build, kill, or fix?
Growth: Where to push — acquisition, activation, referral, or retention
Ops: Do you need to slow down or speed up?
3. Measurement Implications
What metrics reflect movement within the model?
What would signal it’s working (or breaking)?
# Output:
Final output should include:
- The mental model name + metaphor
- A description of how it maps to real growth behavior
- 3–5 ways to use this model in product/growth decision-making
- Suggested metrics or signals to track in the context of the model
- Bonus: Reference similar real-world metaphors (if helpful)Let’s connect. DM me on LinkedIn if you’re building something interesting.
Strategy & Tactical Playbook for Startups and Scaleups
The “Cold Start Problem” Framework:
Chen had helped every founder with his book “Cold Start Problem” – how to launch a new product that relies on network effects.
He introduces Cold Start Theory as five stages every networked product goes through:
1) Cold Start,
2) Tipping Point,
3) Escape Velocity,
4) Hitting the Ceiling,
5) The Moat.
In practical terms, the hardest part is Stage 1, the Cold Start itself, when your product is initially useless because not enough users are there (think of a social network with no friends on it, or a ride-hailing app with no drivers yet).
Chen calls this the effect of “anti-network effects” – new users churn since the value is near zero early on.
His tactical solution is to identify and build an atomic network – a small, tightly-knit unit of users for whom the product is immediately valuable.
For example, Facebook started with a single college campus (Harvard) as an atomic network, Slack found that just 3 active users in a team could form a stable unit, and Uber treated each city as an atomic network to seed from scratch. Chen’s advice is to solve a deep, specific problem for a focused set of users, creating engagement density that can sustain itself, then replicate adjacent networks one by one.
Only after hitting a Tipping Point – where each new network you add (each new city, each new community) comes easier – do you reach Escape Velocity, the stage of self-propelled growth.
Eventually, growth may Hit a Ceiling (saturation or competition limits growth), at which point tactics shift to improving product value or expanding the market. If done right, you end up with The Moat – a wide defensive network effect that competitors struggle to penetrate.
Figure:
Chen’s Cold Start Theory – Each networked product goes through stages:
at first, growth is flat and fragile (Cold Start). With the right seeding of an atomic network, usage hits a Tipping Point where network effects kick in.
Growth then turns exponential (Escape Velocity) until external limits cause it to Hit the Ceiling.
A mature network with high engagement forms a defensible Moat that new entrants can’t easily disrupt.
Founders should diagnose their stage and apply the right playbook – e.g. heavy seeding in early stages, optimization and expansion in later stages.
Drop your thoughts. I reply to every comment…. let’s jam on growth.
AI Prompt #4 - B2B SaaS Product-Led Growth Loop Design
Purpose
This prompt is designed to help B2B SaaS founders and growth teams design a self-reinforcing product led growth loop that drives user acquisition, engagement, and expansion from within the product experience.
You’ll use the PLG loop model to structure how users interact with your product, create value, and organically drive more usage or new users.
It’s ideal for:
Early-stage B2B SaaS startups building virality or collaboration features
Growth-stage products optimizing their PLG flywheel
Teams planning activation, loop instrumentation, or invite mechanics
This framework helps you:
Map out your core PLG loop: Trigger → Action → Output → Reinvestment
Define viral mechanics (K-factor, cycle time)
Spot and fix loop drop-offs
Assign next steps to growth or product teams
Pre-Work: Questions to Prepare Before Running the Prompt
Before using the prompt, you should prepare answers to these questions (or provide their best guesses/observations):
PRODUCT + USER CONTEXT
What is your B2B SaaS product?
Describe the core functionality or outcome. Example: “Internal analytics dashboard for operations teams”
Who is your primary user persona?
Example: “Ops manager at a logistics company”
Can users currently share, invite, or collaborate inside the product?
Yes/No + how
Example: “Yes — users can invite teammates to view and comment on reports”
USER BEHAVIOR INSIGHTS
What is the core action users take to get value? Example: “Create and automate a report using live business data”
What is a typical trigger that gets a user to return or take action? Example: “Gets an alert when a metric changes”
Does anything users create (e.g. docs, dashboards, tickets) get shared externally or internally? Example: “Reports are often exported or shared with cross-functional teams”
What usually happens after they complete a successful action?
Example: “They get feedback or send it to others for review”
VIRALITY & MEASUREMENT (IF AVAILABLE)
Roughly how many users are invited per user per month?
Example: “Each active user sends ~1.2 invites/month”
What % of invited users actually sign up?
Example: “Conversion is ~35%”
How long (on average) between:
user signup → invite
invite sent → new user activated
Example: a) 1.5 days; b) 2.5 days
Summary of What the Prompt Will Do
Once the founder provides the answers (or approximations), the GPT will:
Design a full product led growth loop customized to their B2B SaaS product
Break it down into:
Trigger event
Core action
Output (value)
Reinvestment path (how the loop continues)
And Suggest:
How to measure loop health (K-factor, viral cycle time)
Where to optimize (e.g., earlier sharing, better CTAs)
What the team should focus on in the next 30–90 days
Prompt
Act as a B2B SaaS product led growth strategist with expertise in designing scalable growth loops. Help a founder or growth team create a high-leverage product led growth (PLG) loop for their B2B SaaS product using the classic flywheel model:
Trigger Event → Core Action → Output → Reinvestment Path
# Input:
Ask the Founder for 3 Inputs:
1. Brief description of the product
(e.g., project management for agencies, internal dashboard tool, AI-powered sales platform)
2. Primary user persona
(e.g., operations manager, individual contributor, product lead)
3. Whether users can currently share, invite, or collaborate
(Yes/No + how)
# Steps:
Step-by-Step Loop Design:
## Step 1: Trigger Event
What moment or event naturally prompts a user to take action?
Example: User receives a shared report or completes onboarding
## Step 2: Core Action
What key action delivers value and could generate output/shareability?
Example: Creates a project, sends a proposal, invites a teammate
## Step 3: Output
What result does the user get from the action that’s valuable and shareable?
Example: Completed deliverable, shared dashboard, real-time insights
## Step 4: Reinvestment Path
How does that output loop the user or others back into the product?
Example: Shared item prompts invite, usage leads to expansion, output triggers follow-up workflows
Encourage low-friction reinvestment that activates new users or deepens usage.
# Measurement Layer:
After defining the loop, guide the founder to define how to measure performance:
1. Viral Coefficient (K-Factor)
Formula: avg # of invites × invite-to-signup conversion rate
e.g., 1.5 invites × 40% conversion = K = 0.6
2. Viral Cycle Time
Measure how long it takes to complete a full loop (activation → invite → new user activation)
e.g., 2.5 days per cycle
3. Loop Efficiency
Identify biggest drop-offs (e.g., invite sent but not clicked, signup but no activation)
Recommend improvements (e.g., incentive placement, embedded sharing, better onboarding)
# Output:
- A mapped PLG loop (as a text-based flywheel)
- A list of 3–5 loop optimization ideas
(e.g., move share earlier in workflow, highlight benefit of inviting, gamify output sharing)
- Suggested team focus areas for next 30–90 days
(e.g., loop instrumentation, viral UX testing, onboarding speed)
Use a B2B-specific lens: emphasize value before invite, workflow integration, and bottom-up expansion within organizations.Growth Loops, Not Funnels:
Chen always favors growth loops over traditional marketing funnels.
In a funnel, you pour in leads at the top and hope some convert at the bottom. You often lose momentum once they convert.
In contrast, a growth loop is a closed circuit: users drive more user acquisition. For example, at Uber, satisfied riders invited friends via referral credits, creating a loop where each new rider could bring in others, lowering acquisition cost over time.
Chen popularized the idea that “growth loops are the new funnels.”
Instead of treating acquisition, activation, retention, referral as linear steps (AARRR funnel), design them as a continuous loop.
Example: Dropbox’s referral program is a classic loop – every invite by an existing user (output of one cycle) leads to new users who then invite more (input to the next cycle).
Chen advises founders to map out such loops: What actions do your users take that bring in or activate others? Optimize those.
This might mean building sharing features, referral incentives, or viral content into the core product experience.
The key is reinvestment of output back into input – a self-sustaining cycle. Chen also warns that loops require retention to work; a leaky bucket breaks the loop. Thus, he often says **“retention is the foundation of growth”** – focus on engaging users first so that any viral or paid acquisition effort has compounding returns.
AI Prompt #5 - Funnel vs. Flywheel + GTM Shift
PURPOSE
This prompt helps founders and product/growth teams analyze whether their startup is operating under a linear acquisition funnel or a compounding growth loop (flywheel) — and shows how to transition toward a loop-based GTM model using behaviors that already exist inside the product.
It’s ideal for:
SaaS teams focused on product led growth (PLG)
Founders moving from sales/marketing-led to usage-led motion
Products showing signs of network, virality, or referral potential
You’ll use this to:
Diagnose your current growth model
Spot high-leverage product behaviors that could power a loop
Design a loop-based GTM strategy
Define key metrics like viral coefficient and cycle time
PREP QUESTIONS: What Founders/Teams Should Be Ready to Answer
These questions ensure the GPT can properly analyze your growth motion and propose a customized loop strategy.
FUNNEL METRICS
Visitor-to-signup conversion rate
e.g., “12% of landing page visitors sign up”
Signup-to-activation rate
e.g., “45% complete onboarding and use the product within 24h”
Activation-to-retention or monetization rate
e.g., “30% of activated users convert to paid within 14 days”
Time-to-value (TTV)
e.g., “Users typically reach the first ‘aha’ moment within 10 minutes”
RETENTION + PRODUCT BEHAVIOR
What does your retention curve look like?
Flat? Decay? Improve over time?
Day 1 / Day 7 / Day 30 retention %
Do users tend to refer, invite, or share anything inside the product?
e.g., “Yes — reports and dashboards can be shared with teammates”
Are users collaborating, commenting, or generating content others can interact with?
e.g., “Teams comment on shared proposals and async edit docs”
What behaviors might cause another user to sign up or re-engage?
e.g., “Seeing a shared item, receiving an invite, reacting to a shared insight”
DIAGNOSTIC READINESS
Where do your biggest drop-offs happen in the funnel?
Between sign-up and activation? Activation and retention?
What’s one product action that seems to drive expansion, invites, or ongoing usage?
e.g., “Creating a dashboard and sharing it leads to 2–3 new users added”
WHAT THE PROMPT WILL DELIVER
Once you've provided the above (even in rough or qualitative form), the GPT will return:
1. Growth Model Diagnosis
Clear answer: “You’re operating a [Linear Funnel / Compounding Flywheel]”
Why that diagnosis fits
Signs of emerging loops, if present
2. Loop-Based GTM Strategy
Growth loop structure:
Trigger → Core Action → Output → Reinvestment Path
Description of how the loop works inside your product
Loop entry/exit points for users
3. Loop Metrics
How to measure:
Viral coefficient (K-factor)
Viral cycle time
Drop-off zones inside the loop
4. Tactical GTM Shifts
3–5 experiments or product changes to turn linear motion into a compounding loop
e.g., Invite at value moments, embedded sharing, post-usage reinvestment prompts
5. Team Focus for the Next 30–90 Days
Specific team focus areas (e.g., product virality, loop analytics, activation-to-invite speed)
Prompt: Funnel vs. Flywheel Diagnosis + Loop-Based GTM Design
Act as a SaaS GTM strategist and product-led growth architect with expertise in scaling products using both linear funnels and compounding growth loops. Help a founder or growth team:
1. Analyze their current growth motion (linear vs. loop)
2. Identify existing product behaviors that could power a flywheel
3. Recommend how to shift toward a loop-based GTM model
4. Define loop metrics and team focus areas
# Input:
Ask the Founder/Team to Provide (or Estimate):
Funnel Metrics:
- Visitor-to-signup conversion %
- Signup-to-activation %
- Activation-to-retention or monetization %
- Avg. time-to-value (TTV)
Retention Behavior:
- Day 1, Day 7, Day 30 retention %
- Shape of retention curve (flat, decay, compounding)
- Do retained users tend to invite, collaborate, or expand?
Product Behaviors:
- Are users generating output others interact with? (e.g., shared reports, dashboards, docs)
- Can users invite or collaborate with others in-product?
- Does usage naturally create network or viral exposure?
# Steps: Step-by-Step Analysis
## Step 1: Diagnose Growth Model
If user acquisition depends mostly on external channels, funnel drop-off is high, and retention doesn’t improve over time → it’s a linear funnel
If user actions generate more users, retention strengthens with activity, and usage creates organic growth → it’s a compounding loop
Deliver:
A clear diagnosis: “Your current GTM motion is a [Linear Funnel / Compounding Loop]”
Any signs of early loops forming (e.g., high invite rates, shareable output, async collaboration)
## Step 2: Recommend a Loop-Based GTM Motion
Using existing product behavior, suggest a loop structured like:
1. Trigger Event
What prompts user action?
e.g., reaching a milestone, sharing content, getting feedback
2. Core Action
What action provides value and sets up a reinvestment?
e.g., creating a report, sending a proposal, starting a workflow
3. Output
What result does the user get from this?
e.g., insights, feedback, finished asset
4. Reinvestment Path
How does this action or output attract/retain new or existing users?
e.g., invite others to view, collaborate, contribute
## Step 3: Add Loop Measurement Layer
Help define loop performance metrics:
- Viral Coefficient (avg invites × conversion rate)
- Cycle Time (how fast one user leads to another)
- Activation → Expansion Paths (individual → team → org)
# Output:
- Diagnosis: Linear Funnel or Flywheel
- Visual/Text Loop Structure (Trigger → Action → Output → Reinvestment)
- Loop Metrics to Track
- 3–5 GTM Experiments to Enable Loop
(e.g., share gating, embedded invites, usage-based referrals)
- Team Focus for Next 90 Days
(e.g., virality UX, activation-to-invite time, reinvestment triggers)Retention-First Go-to-Market:
High retention is proof of product/market fit and creates the conditions for organic growth (users who love the product will tell others).
As one growth expert on his blog put it: *“Too many companies ask ‘How do we get more users?’ when they should ask ‘How do we keep the ones we have?’”*. Chen echoes this: if new users churn quickly, any money or effort spent on marketing is wasted. Tactically, this means instrument your product to measure activation and repeat usage.
For example, track the percentage of users who come back the next day, next week, next month. Improve your onboarding and core feature set until you see strong cohort retention (say, 30% of users still active after 6 months, or whatever is healthy in your domain).
Uber applied this by focusing on ride completion and repeat rides per user in each city before scaling ad spend. Similarly, Chen points out that in marketplaces, you must ensure supply and demand are retained (e.g. drivers continue to drive, riders continue to ride) to avoid a collapse after initial promotions.
This “product-market fit first, growth second” mindset guards against the common mistake of blitzscaling a product that people abandon – which leads to the dreaded “sugar rush” growth and steep drop-off.
Marketplace Design and “Hard Side” Acquisition:
Having led Uber’s marketplace growth, Chen is particularly expert in two-sided market tactics.
One of his core strategies is to identify the “hard side” of the network and solve that first. In any marketplace, one side (either supply or demand) is usually harder to attract – often supply (providers) because they need strong incentives to join a new platform with no customers yet.
Chen advises founders to focus your initial efforts on the hard side with targeted tactics.
For example, Uber in a new city might heavily recruit drivers (supply) by offering guaranteed earnings, because riders won’t try the app unless cars are available within minutes. Only once a sufficient supply of drivers is online can Uber start marketing to riders (demand). This “supply-first” seeding was critical in Uber’s playbook.
Similarly, for Airbnb’s launch, the hard side was hosts (no one would browse a site with zero places to stay), so the founders famously “did things that don’t scale” – going door-to-door in New York to recruit hosts and even photographing their apartments to make listings attractive.
Chen formalizes this approach: determine which side of your network has higher activation energy, and find creative hacks or incentives to get them on board.
Sometimes it involves manual outreach, subsidies, or producing content yourself to seed the network (as Reddit did by populating links, or OpenTable did by manually onboarding restaurants).
Design your marketplace in stages:
first solve hard side supply,
then ignite demand,
then transition into a balanced growth mode.
Chen also notes you should protect the hard side’s experience carefully – e.g. ensure drivers make enough money or hosts get bookings – because if your critical supply quits, the network collapses.
AI Prompt #6 - Build a 2-Sided Marketplace (Hard Side, Incentives, Onboarding)
PURPOSE
This prompt helps founders building a 2-sided marketplace identify and solve the early liquidity problem - the classic “chicken-and-egg” dilemma.
Inspired by Andrew Chen’s The Cold Start Problem and real-world examples like Airbnb, Uber, and Upwork, the prompt guides the founder to:
Identify the hard side of the marketplace (supply vs. demand)
Design targeted incentives to attract and retain the hard side
Prioritize onboarding tactics that activate the hard side and trigger initial loops
This is critical for marketplaces at zero-to-one, post-MVP, or early GTM stages.
PREP QUESTIONS — What Founders Should Be Ready to Answer
These questions help you gather the right context before using the prompt.
1. Marketplace Model
What is your marketplace in one sentence?
e.g., “A platform that connects chefs with local event planners”What category are you in?
e.g., services, physical goods, digital assets, knowledge, talent, SaaS integrations
2. User Roles & Sides
Who is the supply side (who provides the value, service, or inventory)?
Who is the demand side (who consumes or pays for it)?
Is the exchange:
1:1 (e.g., coach → client)?
1:many (e.g., content creator → followers)?
many:many (e.g., job board)?
3. Value Creation & Interaction Type
Who creates the inventory or the experience that gets consumed?
Is usage synchronous (live booking)? Asynchronous (deliverables)?
Is there any early traction or pre-seeded side?
4. Current Challenges
Which side is harder to acquire, activate, or retain?
Are there signs of one side churning due to lack of liquidity?
WHAT THE PROMPT WILL DELIVER
Once answers are provided, the prompt will return:
1. Hard Side Diagnosis
Which side (supply or demand) is harder and why
Strategic rationale using Andrew Chen’s network dynamics
Which side should be seeded or subsidized first
2. Early Incentive Strategy
2–3 incentive ideas targeted to the hard side
Options across monetary, product, time-to-value, and social motivators
How to ensure incentives spark real engagement without long-term distortion
3. Onboarding Tactics for the Hard Side
3–5 specific onboarding tactics
Approaches like:
Concierge onboarding
Ghost listings or seed liquidity
Community spotlighting
Role-based templates or starter kits
Designed to get users to their first success fast
4. (Optional) Real-World Analog Examples
Examples from Uber, Airbnb, Thumbtack, Upwork, OpenTable
Used to model your own strategy
USE CASES
Early GTM strategy planning
Marketplace onboarding redesign
Investor pitch prep (showing your seeding logic)
Pre-seed or seed-stage product-market loop design
Prompt:
Act as a hybrid of Andrew Chen, a 2-sided marketplace founder, and a network effects strategist. Help a startup team build a 2-sided marketplace from the ground up by identifying:
- The hard side of the marketplace (supply or demand)
- Smart early incentives to attract and retain that side
- Prioritized onboarding tactics to activate the hard side and trigger liquidity
# Inputs:
Ask the Founder for These Inputs:
1. What is your marketplace?
- Describe the category or product in 1–2 sentences
Example: “A platform connecting freelance copywriters with marketing teams”
2. Who is your supply side? Who is your demand side?
Example: Supply = copywriters; Demand = marketing managers at startups
3. What kind of exchange or interaction happens?
- Is it 1:1, 1:many, or many:many?
- Is the transaction time-sensitive or persistent?
4. Where does value originate?
- Who creates the “inventory” (e.g., content, availability, listings, services)?
5. Do you have any early traction or seeding activity?
- e.g., “We’ve onboarded 30 writers but have no paying clients yet”
# Steps:
## Step 1: Identify the Hard Side of the Marketplace
Diagnose which side is the “hard side” — the one that’s more difficult to:
- Attract initially
- Retain over time
- Deliver a good experience without sufficient liquidity
Consider:
- Who has higher opportunity cost?
- Who drives the initial perception of value?
- Who is harder to replace if they churn?
Output a clear diagnosis: “The hard side is [X] because...”
## Step 2: Design Early Incentives for the Hard Side
Recommend 2–3 incentives to attract and retain the hard side of the marketplace. These could include:
- Monetary: subsidies, minimum earnings, signup bonuses
- Product-based: access to tools, free features, analytics
- Social: visibility, exclusivity, badges, referrals
- Time-to-value: guaranteed matches, concierge onboarding
Make sure incentives:
- Are targeted (only offered to the hard side)
- Do not permanently distort user expectations
- Create momentum toward usage and referrals
## Step 3: Prioritize Onboarding Tactics for the Hard Side
Recommend 3–5 onboarding tactics tailored to the hard side. These should help:
- Activate the user (get to value fast)
- Seed content or supply
- Create the perception of activity and liquidity
- Encourage feedback loops (e.g., invite others, get ratings)
Suggest tactics such as:
- Manual onboarding or “concierge” setup
- Pre-loading the supply side with data or starter tasks
- Creating ghost demand/supply (fake listings, auto-pings) to simulate engagement
- Community seeding (Slack group, webinars, spotlighting early users)
# Output:
Final output should include:
- Hard side identified + rationale
- Early incentive structure
- Onboarding plan (sequenced tactics for early traction)
- Bonus: real-world analogs (e.g., Uber = drivers first, Airbnb = hosts first, Upwork = freelancers first)Bonus AI Prompts
AI Prompt #7 - Define Your Product’s Atomic Network
Purpose
This prompt is designed to help founders and growth/product teams define the atomic network for their product — the smallest self-sustaining unit where users create and derive value from each other. By identifying and seeding this core network, you can build a self-reinforcing product experience that scales efficiently.
The goal is to:
Identify the smallest unit of users who will interact with the product and generate value.
Understand who to onboard first to ignite the atomic network and create viral loops.
Optimize the product’s usage and collaboration dynamics to drive organic growth and usage.
This is ideal for:
Early-stage startups with network-driven products, multiplayer experiences, or collaborative tools.
Founders and product teams working on product-led growth (PLG) strategies.
Teams who want to move beyond linear acquisition funnels and create self-sustaining growth loops.
Founder/Team Prep Questions (What to be ready with)
These are the questions to help gather the necessary context, usage patterns, and behaviors. Having answers to these will ensure the prompt is actionable and provides deep insights.
1. Product Context
What is your product?
Provide a brief description of what your product does and the core problem it solves.
Example: “A project management tool for remote teams to collaborate on client work.”
Who is your target user?
Who uses your product, and what roles do they typically hold?
Example: “Operations managers, team leads, and project coordinators.”
2. Usage Context
When and how do users typically engage with the product?
Is it used synchronously (e.g., real-time collaboration), asynchronously (e.g., feedback on a document), or via event-based triggers (e.g., a user receives a notification)?
Example: “Used mainly for weekly project check-ins and task management during team standups.”
Does your product support group activities (e.g., teams, classes, projects), or is it more individual (e.g., personal productivity)?
Example: “Used by teams to create tasks and assign team members.”
3. Minimum Viable Collaboration
What is the minimum group size or unit where users find value?
What size or combination of users are needed to achieve a meaningful outcome with your product?
Example: “A project team of 2-5 people collaborating on one project or task.”
Do users need to interact with each other to derive value, or can they work independently?
Can users get value individually (e.g., without collaboration) or is interaction between users required?
Example: “Users can complete tasks solo but benefit more when they collaborate on shared projects.”
4. Intra-User Dependencies
How do users rely on each other in your product?
Are there dependencies between users (e.g., one user assigns a task, another completes it)?
Example: “Managers assign tasks; team members complete them and update status.”
Are there roles within the product (e.g., creator vs. consumer, leader vs. follower, moderator vs. participant)?
Example: “Project owners assign tasks, while collaborators execute tasks and provide feedback.”
5. User Actions and Network Behavior
What user actions generate or depend on other users?
For example, does sending an invite, sharing content, or providing feedback lead to other users joining the product or re-engaging?
Example: “When a user assigns a task, the team member invited must engage with it for progress.”
Do users generate content or actions that drive network behavior (invitations, shares, engagement)?
Example: “Sharing a task list or project board with others leads to new team member sign-ups.”
What the Prompt Will Deliver
Once you/founder/team answers the above questions, the GPT will:
Define the atomic network: The smallest, self-sustaining unit of users that creates and derives value from each other (e.g., a project team, a study group, etc.).
Recommend who to onboard first: Based on the atomic network, suggest the ideal first users to kickstart the network effect (e.g., team leaders, content creators, power users).
Recommend onboarding flows: Design strategies to activate users quickly and get them to the point of creating value for others (e.g., onboarding via shared goals, pre-filled templates).
Prompt:
Act as a network effects strategist, product-led founder, and systems-level thinker with expertise in designing early-stage networks, collaboration loops, and self-sustaining user ecosystems.
Help a founder define the atomic network for their product — the smallest self-sustaining unit of users that creates and receives value through networked usage. This will clarify who to onboard first and how to scale in loops, not lines.
# Inputs:
Ask the Founder These 3 Things:
1. Usage Context
When and where does the product get used?
Is usage synchronous (real-time), asynchronous, or event-based?
What’s the job the product helps users accomplish?
Example: “Project tracking tool used by agency teams daily during standups”
2. Minimum Viable Collaboration
What is the smallest group of users that can interact and derive mutual value?
What specific actions do they take with or for each other?
Can one user get value alone, or do they need others to activate usage?
Example: “2+ users commenting on and updating a shared client dashboard”
3. Intra-User Dependencies
How are users connected?
Viewer vs. editor? Creator vs. responder?
Is the interaction one-way, bidirectional, or multi-node?
Does the value compound as more users interact?
Example: “A manager assigns tasks → team completes → status updates flow back”
#Task:
## 1. Define the Atomic Network
Based on their inputs, define the atomic unit of networked value — e.g.:
- A team working on a project
- A classroom using the same materials
- A shared document + collaborators
- A feedback loop between creator and audience
Explain:
- Why this unit is self-sustaining
- What interactions must occur to keep it alive
- What triggers initial and ongoing engagement
## 2. Recommend Onboarding Strategy
Recommend who to onboard first to activate the atomic network:
1. Role: Who is best positioned to invite others, create shared value, or trigger reinvestment?
e.g., Team leads, content creators, power users, educators
2. Rationale:
- Are they central to usage or coordination?
- Do they have access to a group that completes the loop?
- Do they naturally generate network behavior (invites, sharing, collaboration)?
## 3. Next Steps:
Suggest how to design onboarding or seeding flows for this user type
Include how to get them to full network activation quickly (e.g., prompt to invite, pre-loaded workspace, shared goals)
Use strategic clarity, product-growth framing, and network dynamics. Reference real-world analogs when relevant (e.g., Slack: team = atomic unit, Notion: doc + collaborators, Zoom: host + meeting participants).AI Prompt #8 - Select the Right North Star Metric for a SaaS Product
PURPOSE
This prompt helps SaaS founders and growth teams define the right North Star Metric (NSM) based on:
Product category (B2B vs. B2C, single-user vs. collaborative)
User journey stages (activation, value, retention)
Core product behavior or usage loop
The North Star Metric acts as your true north — guiding product, marketing, and growth teams toward scalable value creation. It replaces vanity metrics with a measurable, retention-aligned signal of actual product impact.
PREP QUESTIONS — What You Should Be Ready to Answer
These questions prepare the inputs the AI (or growth advisor) needs to produce a strong recommendation.
PRODUCT CONTEXT
What does your product do, and who is the core user?
Example: “Workflow automation for HR teams”
What core job does it solve for the user?
Example: “Reduces time to complete onboarding paperwork”
What is the most important user behavior or action in your product?
Example: “Creating an onboarding checklist and assigning tasks”
USER JOURNEY & BEHAVIOR
What are the stages of your user journey?
Awareness → Signup → Activation → Value → Habit → Expansion
Where are the biggest drop-offs?
Is your product used solo or collaboratively?
Single-player (e.g., note-taking)?
Multi-player (e.g., project management, CRM)?
Does the product depend on recurring usage or one-time setup?
METRICS YOU TRACK NOW
What input metrics do you currently track?
e.g., tasks created, dashboards shared, messages sent
What outcome metrics do you track?
e.g., weekly active users, MRR, retention %
WHAT THE PROMPT WILL DELIVER
Once you provide these inputs, the GPT will return:
1. North Star Metric Recommendation
The most strategic metric based on:
Product usage loop
Value realization
Retention correlation
2. Metric Breakdown
Input metric (leading indicator)
Outcome metric (lagging indicator)
When to track each, and how to connect them
3. Strategic Rationale
Why this metric matters
How it reflects user success
How it ties into loops (e.g., collaboration, sharing, return triggers)
4. Team Alignment Use
How to use the NSM to align product, marketing, and growth teams
Whether to layer it with team-level metrics (e.g., WAUs per workspace, shared docs per week)
Prompt:
Act as a SaaS growth strategist, product analytics lead, and PLG advisor. Help a founder or team select the right North Star Metric (NSM) based on their user journey, core product behavior, and category (e.g., single-player SaaS, collaborative tools, usage-based platforms).
The NSM should represent value delivered, not vanity growth — and ideally drive or reflect retention and growth loops.
# Inputs:
Ask the Founder for the Following Inputs:
1. What is your product and who is it for?
Short description + user type Example: “A goal-tracking app for remote teams”
2. What is the core value or job the product delivers?
The “why it exists” from the user’s POV Example: “Helps teams align on OKRs and track progress async”
3. What is the core action users take to unlock value? Example: “Create and review a goal with teammates weekly”
4. What are the key stages in the user journey?
Awareness → Activation → Value → Habit → Expansion
5. Where are the biggest drop-offs today?
6. What metrics do you currently track?
- Input metrics (e.g., goals created, invites sent, files uploaded)
- Outcome metrics (e.g., weekly active teams, retention %, NPS, revenue)
# Task: Recommend a North Star Metric
Define:
1. Candidate Input Metric
- A leading indicator of value creatione.g., “Goals created per team per week”
2. Candidate Outcome Metric
- A lagging indicator of value realized or retainede.g., “Teams active for 4+ consecutive weeks”
3. Recommended NSM
Choose the metric that best balances:
- Relevance to core user value
- Measurability and actionability
- Tie to retention, loops, or expansion
Explain:
- Why it aligns with the product’s behavioral loop
- How it acts as a compass for teams (product, marketing, growth)
- Whether it should be layered (input + output) or singular
# Output:
Output should be strategic, practical, and tied to retention or product-led growth loops.Founder Takeaways – Chen’s Tactical Checklist:
To apply Andrew Chen’s tactics in your startup, consider this high-signal checklist:
Identify Your Atomic Network: Define the smallest group of users that find core value in your product. Focus your early features and outreach exclusively on them until you see strong engagement.
Founder’s exercise: Write down “My atomic network is ___ because these users need each other/product in ___ way.”Engineer a Growth Loop: Don’t rely solely on linear marketing.
Design a feature or incentive that turns 1 user into >1 new users (virality, referrals) or increases usage (user-generated content, network invites). Instrument it so you can track the viral coefficient (k-factor). Even a modest loop (e.g. each user invites 0.4 new users on average) can compound significantly.Prioritize Retention: Before spending big on acquisition, hit a benchmark retention rate that proves users genuinely stick.
For example, aim for a flattened retention curve after a few months – a sign of a loyal user base. Use tactics like lifecycle emails, in-app tutorials, and community forums to boost retention of early users. As Chen says, growth = maximization of user-weeks over time, not just first-week signups.Leverage Community Early: Don’t wait to build a community around your product. Even at 5 employees, start a user feedback group, a Slack community, or weekly user calls.
Early evangelists can be your marketing force (as Figma’s were), and they’ll give you honest product insights. Empower them with recognition or sneak peeks.Run Fast Experiments: Adopt a scrappy testing mentality. Try new landing pages, pricing, referral messages in quick cycles.
Use the two-way door rule – if an experiment can be rolled back, don’t over-debate it, just test it. The learnings are more valuable than lengthy deliberation. Make sure each experiment has a clear success metric (OKR alignment) so you know if it worked.Document Your Playbook: As you find things that work, write them down! Create a simple growth playbook that includes your metrics dashboards, user personas, best channels, and key tactics.
This not only onboards new team members faster, it forces you to articulate your strategy crisply (just as Stripe’s memos sharpen thinking). Revisit and revise the playbook regularly as the product evolves.
By systematically applying these tactics, even a small startup can punch above its weight in growth. Chen’s playbook is essentially about building momentum through loops and locking in success through retention. Every tactic you try should feed into that momentum or strengthen the product’s value, or preferably – do both.
If this sparked new ideas, explore the full set of prompts tailored to apply Andrew Chen’s playbook to your startup. I’d love to hear how you’re using them - reply, comment, or share your take. Let’s exchange notes on building smarter, faster, and more sustainably.
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