AI advisors modelled on practitioners
@andrewchen
AI interpretation, not the person. This advisor is modelled on Andrew Chen's publicly available work and documented frameworks. It is not Andrew Chen. It does not speak as Andrew Chen, it does not fabricate quotes attributed to Andrew Chen, and it will not reproduce copyrighted material. Every response cites the public source it draws from.
Quantitative, data-driven growth with a focus on viral mechanics and network effects
This is an AI interpretation of Andrew Chen's public work. Andrew Chen is a general partner at Andreessen Horowitz and author of The Cold Start Problem. His public writing focuses on viral growth, network effects, and product-led growth for consumer and marketplace products. His frameworks are documented in his book and public blog at andrewchen.com.
The viral coefficient (k-factor) measures how many new users each existing user brings. k = (invitations sent per user) x (conversion rate of invitation). k > 1 means exponential growth; k < 1 means growth depends on other channels. Most consumer apps achieve k = 0.15 to 0.4; k > 0.7 is considered strong. The k-factor is a product metric, not a marketing metric: it is determined by when you trigger sharing, what the invite looks like, and the incentive structure.
Growth accounting decomposes MAU growth into four buckets: new users (never seen before), retained users (active last period), resurrected users (churned but returned), and churned users (active last period, not this period). Net growth = New + Resurrected - Churned. This framework surfaces whether growth is health (new + retained) or fragile (new masking churn). A product with high new-user additions but high churn is a leaky bucket.
In domain
When a question falls within growth, viral-loops, product-led-growth and related areas, this advisor answers grounded in Andrew Chen's documented frameworks, with citations to the public source.
Out of domain
Out of domain, this advisor explicitly flags low confidence and defers rather than guessing. It never answers authoritatively outside its declared specialisation.
Example
“How do I build a paid social campaign on Meta for DTC?”
This question is about paid-social which is outside this advisor's declared domains.
Capabilities
3Schedule
Source scraping
onWeb research
onRecent runs
+12doneAdd to knowledge
Hypothesis: most referral programs fail because they optimise for invitation volume rather than invitation quality. High-volume, low-fit referrals increase k-factor short-term but damage retention and LTV. The better design is to optimise for referred-user activation and 90-day retention, not invitation volume. Note: this is a working hypothesis from pattern-matching across cases, not a rigorously validated framework.
The document explores how growth hackers can transition into and fulfill VP of Marketing roles, outlining the key responsibilities they take on and how their data-driven, experimental approach can significantly accelerate startup growth.
This 80-slide deck is designed to help founders understand how investors evaluate startups by highlighting the key metrics and warning signs that investors look for during the investment process.
The document is a 70-slide deck from a16z (Andreessen Horowitz) that outlines the firm's criteria for evaluating consumer startups, providing guidance on what characteristics make founders and products compelling investment opportunities.
The document provides a concise retrospective on viral loops, drawing on lessons learned (and subsequently forgotten) during the Web 2.0 era, highlighting how products and services spread organically through user-driven mechanisms.
The document examines frequently encountered skeptical memes associated with various product categories and provides concise, one-line rebuttals designed to help counter critics of those products.
The document shares lessons derived from analyzing thousands of users and applies those insights to the specific challenge of retaining users in modern AI applications.
The document is a brief self-description from a newsletter author who writes long-form essays on startups, user growth, and network effects, and has also authored a best-selling book called "The Cold Start Problem."
The document introduces the topic of viral growth as it relates to product features, highlighting its role as a central driver behind many of the world's most popular products reaching massive user bases.
The document is an article by Andrew Chen exploring viral growth as a product-driven mechanism — specifically the kind embedded in features such as invites, sharing, and collaboration. The article argues that the best approach to driving viral growth is to focus on retention and engagement rather than purely on acquisition.
The document is a personal announcement by the author about joining Andreessen Horowitz (a16z) as a General Partner, based in San Francisco, CA. The author focuses on consumer startups spanning marketplaces, entertainment/media, and social platforms, and expresses enthusiasm about their new role as a professional investor.
The document provides a brief profile of Andrew, a prolific writer and author associated with a "speedrun initiative." Over the past decade, he has written hundreds of essays on startups and user growth, and has authored a best-selling business book published by a major publisher.
The document discusses the limitations and pitfalls of viral loop strategies for product growth, arguing that they are largely ineffective in the modern landscape.