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Network Effects for AI Products: What Actually Works vs. What VCs Tell You

Every pitch deck I see has a slide about network effects. "As more users join, the product gets better for everyone." It's the dream: a self-reinforcing loop that creates an unbeatable moat.

But here's what I've learned building AI products after spending years at Meta watching real network effects at scale: most AI products don't have meaningful network effects. And the ones that do work very differently than founders think.

What Real Network Effects Look Like

At Meta, I worked on ads measurement systems that processed hundreds of billions of events daily. The network effect there was obvious: more advertisers → more competition for ad slots → better prices for Meta → better content for users → more users → more valuable ad inventory → more advertisers.

Each loop was measurable. We could quantify exactly how much value an incremental user added to the platform. The math worked at any scale from 10M users to 3B users.

When I started building AI tools for creators, I thought I'd replicate that pattern. Get enough creators using the tool, their content would train better models, which would help new creators, which would bring more creators. Perfect loop, right?

Wrong.

Why Most AI Products Don't Have Network Effects

Here's the uncomfortable truth: AI products usually have centralized improvement, not network effects.

When you make your model better, everyone benefits equally. A new user doesn't make the product better for existing users—your engineering team making the model better does that.

Let me break down the common myths:

Myth 1: "More Users = Better Model = Better Product"

This is what founders call "data network effects." The idea is that more users generate more data, which improves your model, which attracts more users.

The problem? This loop is:

  1. Slow - You need months/years of data to see meaningful model improvements
  2. Not user-specific - The 100,000th user doesn't directly benefit the 100,001st user
  3. Easily replicated - Big tech can train on more/better data than your startup will ever collect

I've seen startups spend 2 years collecting training data, only to have OpenAI or Anthropic release a foundation model that's better trained on the entire internet.

Myth 2: "User-Generated Content Creates Network Effects"

This one's partially true but mostly misunderstood.

Yes, if users create content that other users consume, that's a network effect. But the AI part usually isn't the network effect—the content is.

Example: A tool where creators use AI to generate video hooks, and other creators can browse/remix those hooks. The network effect is in the content library, not the AI. You could replace the AI with human-written hooks and the network effect would still exist.

The AI is a feature, not a moat.

Myth 3: "Collaborative Features = Network Effects"

Adding multiplayer or sharing doesn't automatically create network effects. It creates collaboration features.

Real network effects mean each new user makes the product more valuable for existing users. Collaboration means a small group works better together. That's useful but doesn't scale the same way.

What Actually Works: Data Flywheels, Not Network Effects

Instead of chasing network effects, I focus on data flywheels. They're different and more achievable for small teams.

Data Flywheels in Practice

Here's what works in our creator tool:

  1. User creates content using our AI
  2. We see which AI-generated options they choose
  3. We learn what good looks like for that specific creator
  4. We personalize future suggestions for them
  5. They get better results, create more content, we learn more

Notice the difference? The loop is per-user, not cross-user. User A's data makes the product better for User A, not for User B.

This is achievable for a small startup because:

  • You don't need millions of users to start
  • Each user gets value from day 1
  • Your data is personalized, not generic
  • Big tech can't easily replicate your understanding of individual users

Where This Works Best

Data flywheels are powerful when:

1. Personalization matters more than absolute quality

  • A video hook generator that learns YOUR voice
  • A code completion tool that learns YOUR patterns
  • A writing assistant that matches YOUR style

2. The feedback loop is fast

  • User sees result, accepts or rejects, you learn immediately
  • Not "wait 6 months to retrain the model"

3. The data is dense and specific

  • Not "more videos," but "which frames user A reacts to"
  • Not "more text," but "which phrases user B actually publishes"

The Only Real Network Effects in AI Products

There ARE some AI products with true network effects, but they're rare:

1. Marketplace/Platform AI

If your AI connects people who benefit from each other's presence, that's a network effect.

Example: An AI that matches freelancers to projects. More freelancers → more project coverage → more companies → more projects → more freelancers.

The AI is the matching layer, but the network effect is the marketplace.

2. Collective Intelligence Products

If users explicitly improve the product for each other, that works.

Example: Waze. Users report traffic, everyone's route gets better. The data collection is distributed and the benefit is immediate and cross-user.

In AI: A tool where users label/correct AI outputs and those corrections improve the model for everyone. But this requires:

  • Users willing to do the labeling work
  • Fast model update cycles
  • Clear incentives for contributing

Most consumer AI products don't meet these criteria.

3. Community-Powered AI

If the value is in the community and AI enables it, that can work.

Example: A platform where AI helps people ask better questions, and those questions/answers build a knowledge base that attracts more experts.

But again, the network effect is the community, not the AI.

What This Means for Your AI Product

Based on building in this space and watching what actually works:

If You're Pre-Product/Market Fit

Don't optimize for network effects yet. Focus on:

  1. Solving a real problem with AI
  2. Being 10x better than the non-AI alternative
  3. Building a fast feedback loop between usage and improvement

The data flywheel for individual users is your competitive advantage, not cross-user network effects.

If You're Post-PMF

Ask yourself:

  • Does user A's presence make the product better for user B?
  • Is that effect measurable and significant?
  • Does it compound over time?

If no to any of these, you don't have network effects. You have:

  • A good product that gets better with engineering effort (great!)
  • A data flywheel that personalizes per user (excellent!)
  • Viral growth from happy users (wonderful!)

These are all valuable. They're just not network effects.

What to Build Instead

Focus on compounding advantages that don't require massive scale:

1. Vertical-Specific Intelligence

  • Be the best at X for Y audience
  • Your 1,000 power users in a niche beat 1M generic users

2. Integration Lock-In

  • Become part of their workflow
  • The switching cost is retraining the AI on their preferences

3. Human-AI Collaboration Loops

  • Build tools where humans guide AI, not replace them
  • The combined output is the moat, not the AI alone

4. Community + AI

  • Use AI to make community more valuable
  • The community is the network effect, AI is the enabler

The Reality Check from Big Tech

At Meta, we had real network effects. I could show you graphs where user N made the product measurably better for user N+1. The math was clear.

We also had AI products that improved over time but didn't have network effects. They just had good engineering and lots of compute.

Most startups I talk to have the latter but pitch it as the former. VCs love network effects, so founders force the narrative. But the most successful AI products I've seen in the wild are winning on:

  • Execution speed
  • Taste and UX
  • Vertical focus
  • Fast feedback loops

Not network effects.

The Path Forward

If you're building an AI product, here's my advice:

  1. Stop forcing the network effects narrative. Build something useful first.
  2. Focus on data flywheels. Per-user personalization is more achievable and defensible.
  3. Look for real community/platform opportunities. If you can build a true two-sided market or community, AI can make it better. But the network effect is the market/community, not the AI.
  4. Move fast. In the absence of network effects, speed of execution is your moat. Ship, learn, improve faster than anyone else.

The best AI products I've seen don't win because of network effects. They win because they solve real problems elegantly, get better for each individual user over time, and ship improvements faster than the competition can copy them.

That's hard enough. And it's enough.

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