Product Strategy for Gen AI Native Apps

Building Gen AI Native Apps: A Strategic Guide

November 3, 2024

Here's a counterintuitive truth about generative AI startups: the most successful ones won't be the "AI version" of existing products. While everyone races to add AI features to their apps, a new class of products is emerging - ones that couldn't exist without foundation models at their core. These are "Gen AI native" applications, and building them requires rethinking traditional startup playbooks.

The Generative AI Market Map by Sequoia
The Generative AI Market Map by Sequoia

What Makes an App "Gen AI Native"?

A Gen AI native application is one where the core value proposition is delivered through a foundation model, typically a large language model (LLM). This doesn't mean the entire application experience must revolve around the LLM, but rather that the fundamental value creation happens through it. Think of it like database-powered web applications of the past - while the database isn't the only component, it's the essential engine driving the application's core functionality.

For example, while Notion adding AI features makes it "AI-enhanced," a product like Harvey is Gen AI native because its core value proposition (legal research) is impossible without the foundation model.

The Defensibility Paradox

A common misconception is that Gen AI native apps aren't defensible. The reality is more nuanced - defensibility depends largely on where you choose to build. The key is to avoid building solutions for tasks that LLMs already handle well (like basic writing assistance) and instead focus on the "jagged edge" of their capabilities.

Consider Polymet: rather than competing with Claude on general code generation, they focused on product design with deeper workflow integrations and verticalized product experiences. This approach not only creates immediate differentiation but also positions them to evolve as the technology improves.

Best Practices for Building Gen AI Native Apps

1. Embrace Vertical Focus

Success in this space typically comes from highly verticalized applications focused on specific user personas. Horizontal solutions are more likely to be integrated into the model layer itself or first-party apps like ChatGPT, Claude, or Gemini.

Examples of successful vertical focus:

  • Harvey AI focusing specifically on legal research and document analysis
  • Enterpret concentrating on customer feedback analysis
  • Mindshow specializing in AI-powered animation for education

2. Build Unique Data Moats

While moat-building isn't entirely different from traditional applications, Gen AI native apps must capture unique engagement data through their core user interactions. This data can then be used to improve products and services over time.

Successful approaches include:

  • Building proprietary datasets through user interactions
  • Creating feedback loops that improve model performance
  • Developing domain-specific training data
  • Building customer-specific fine-tuning capabilities

3. Rethink Distribution Strategy

Unlike previous platform shifts (on-prem to cloud, desktop to mobile), nobody is sleeping on the AI revolution. This creates a unique challenge: if your value proposition is simply "the AI version of Company X," you're likely to face strong headwinds, as established companies are already building their AI capabilities internally.

Instead, focus on:

  • Creating highly specialized solutions for specific verticals
  • Building go-to-market motions that differentiate from horizontal players
  • Delivering discrete, unique value to your core persona
  • Using this as a wedge to solve broader problems for your customers

4. Establish Early Business Models

Gen AI native companies need to figure out their business models earlier than traditional software startups. While API costs are dropping rapidly, they still represent a significant marginal cost compared to traditional cloud software.

Key considerations:

  • Factor in API costs when pricing your product
  • Consider hybrid approaches using smaller, specialized models
  • Build in scaling efficiencies early
  • Plan for margin improvements as model costs decrease

Building Systems of Record

One particularly promising approach is creating systems of record for previously unstructured domains. These opportunities exist in areas where traditional software hasn't been able to effectively capture and organize information, but Gen AI makes it possible.

Examples include:

  • Unstructured customer interaction data
  • Complex project documentation
  • Institutional knowledge capture
  • Professional service delivery documentation

Common Pitfalls to Avoid

  1. The "AI Everything" Trap Don't try to solve every problem with AI. Focus on specific use cases where AI provides 10x improvement over existing solutions.
  2. Ignoring User Experience Many founders focus too much on model capabilities and not enough on making them accessible and useful. The best AI is often invisible.
  3. Insufficient Domain Expertise Technical AI knowledge isn't enough. Deep understanding of your target industry is crucial for building truly valuable solutions.
  4. Overreliance on Public Models While starting with public models makes sense, have a plan for differentiation through fine-tuning or proprietary models.
  5. Neglecting Privacy and Security Build with data privacy in mind from day one. It's much harder to retrofit later.

The Path Forward

The most successful Gen AI native applications will be those that:

  • Target specific verticals with clear pain points
  • Build unique data advantages through user interactions
  • Create defensible positions through specialization
  • Establish sustainable business models early

Next Steps for Builders

  1. Identify Your Vertical: Choose a specific industry or use case where Gen AI can provide transformative value.
  2. Talk to Users: Conduct at least 20 user interviews to validate your assumptions about pain points and willingness to pay.
  3. Prototype Fast: Build a minimal viable product that demonstrates clear value, even if using basic model integrations.
  4. Plan Your Moat: Design your product to capture valuable training data from day one.
  5. Test Economics: Validate your business model assumptions with real usage data early.

Remember: the goal isn't to be everything to everyone, but to be indispensable to someone. The most successful Gen AI native apps will be those that solve specific, high-value problems in ways that weren't possible before this technology existed.