The technology landscape has become a battlefield. In the past 45 days alone, we've witnessed an unprecedented wave of product launches that would have been unimaginable just a year ago. Notion launched competitors to Granola, Glean, and ChatGPT. Figma entered the territory of Canva, Framer, and Illustrator. Atlassian, Anthropic, Google, and OpenAI all launched competing products across multiple categories simultaneously.
This isn't just isolated chaos—it's the new reality across every software category. Meanwhile, established companies are collapsing faster than ever before. Chegg declined over 90% in mere months, and Stack Overflow became an early casualty of the ChatGPT revolution.
Among all this disruption, one question emerges as critical for product teams: What do I build and why will it win?
The Competitive Battlefield
Today's competitive environment resembles a complex battlefield with multiple fronts. Fast-moving incumbents like Microsoft, Google, and Meta dominate vast territories. New horizontal platforms such as ChatGPT and Anthropic are consuming entire use cases. Foundational technology shifts occur monthly rather than yearly, while hordes of well-funded startups flood every category with traction.
Navigating this environment requires more than tactical execution—it demands strategic insight that others aren't acting upon. As Sean Klaus, Chief Product Officer at Confluent and former CPO at MuleSoft, explains: "You're constantly trying to get ahead. You're trying to find the angle, the question that has not yet been asked that gives you an insight that is not being actioned by other people."
The insight alone isn't enough—it must be an insight that competitors aren't pursuing. This gap between insight and action becomes your competitive advantage.
Avoiding the Common AI Product Traps
Most teams fall into one of two extremes when building AI products. The first trap involves reinventing the AI wheel by building custom models and infrastructure from scratch. This approach is resource-intensive and often unnecessary.
The opposite trap is equally problematic: simply copying and pasting basic AI features like generic chatbots into existing products. This creates shallow differentiation that competitors can easily replicate.
The winning strategy lies in the middle—treating AI like a series of Lego blocks. Success comes from assembling differentiated AI features by integrating the best available AI capabilities with your unique assets: your data, your functionality, and your understanding of unmet customer needs.
Your Competitive Advantage: What's Uniquely Yours
Your competitive advantage doesn't come from the AI itself—everyone has access to the same foundational models. Instead, it stems from three proprietary elements:
- Your Data: The unique context that powers differentiated outputs
- Your Functionality: The specialized workflows and integrations within your product
- Your Customer Understanding: Insights into unmet needs that others haven't identified
The Anatomy of Winning AI Products
Building differentiated AI products requires understanding how to assemble multiple types of "Lego blocks" into a cohesive system.
AI Capabilities: The Foundation Blocks
The first layer consists of AI capabilities—pre-trained models, task-specific abilities, audio processing, image recognition, and other "magical" functionalities that weren't possible before. While these capabilities form the foundation of any AI product, they don't provide differentiation since everyone has access to them.
Your Data: The Context Layer
Your data provides context that enables AI models to generate unique outputs. The more distinctive your data, the more unique value you can deliver to customers. Several types of data can create this differentiation:
- Real-time data that models haven't incorporated into their training sets
- User-specific data that personalizes outputs for individual customers
- Domain-specific data for specialized industries like legal or healthcare
- Human judgment data including curation and reinforcement learning
The key isn't data quantity—it's the marginal value your data adds over what's already trained in foundational models.
Your Functionality: The Control Layer
Your product's functionality determines how AI behaves and gives your AI product its unique superpowers. This includes specialized workflows, proprietary algorithms, business rules, and integrations that are baked into your product.
These elements work as an interconnected system. Your data informs the AI's understanding and helps generate unique outputs, which in turn creates additional unique data in a continuous flywheel. Meanwhile, your functionality controls how the AI takes actions and interacts with users, while increasingly allowing AI to call tools within your product itself.
Case Study: How Granola Found Its Seam
Granola provides an excellent example of finding competitive differentiation in a crowded market. When they entered the AI note-taking space, established players like Fathom, Otter, and Fireflies already dominated the market, alongside incumbent platforms like Zoom and Google Meet with native AI capabilities.
Despite this competition, Granola secured significant funding and market attention by identifying an unmet customer need that others overlooked.
Identifying the Unmet Need
While competitors focused on replacing the entire note-taking job—promising to "take your meeting notes for you"—Granola identified a different customer desire: "I don't want you to take all of my notes. I just want you to help me take better notes."
This insight shifted the product from replacement to empowerment, targeting users who wanted assistance rather than automation.
Assembling the Lego Blocks
Granola built their solution using off-the-shelf AI capabilities—Deepgram for transcription and Anthropic/OpenAI for other functionality. Their differentiation came from how they assembled these components:
Data Assembly: Granola combines user-written notes with AI-generated transcription to create unique context. The AI enhances user notes rather than replacing them, creating a repository that enables additional features like cross-meeting chat and project workspaces.
Functionality Integration: They built a Mac application that detects meeting starts and accesses system sound for seamless transcription. Calendar integrations provide meeting metadata like attendees, while the native app ensures AI activation precisely when users need it.
This system creates a flywheel where enhanced notes become data for future enhancements, while specialized functionality enables unique AI behaviors.
The Sequencing Challenge
Granola's initial success demonstrates that assembling the right Lego blocks can create market differentiation. However, maintaining competitive advantage requires continuous sequencing—leveraging initial building blocks to create new unique combinations.
Granola continues this evolution by expanding into project workspaces, integrating downstream actions like CRM connections, and experimenting with features like auto-updating company wikis. Each addition builds upon their foundational differentiation while creating new moats.
The Reality of Modern Competitive Moats
Traditional competitive moats that lasted 6-12 months now provide protection for only 2-3 weeks. As Jamin Ball from Altimeter Capital notes: "The real moat is just a sequence of smaller moats stacked together. Each one buys time. What you do with that time, how fast you execute, how quickly you evolve determines whether you stay ahead."
This reality transforms product strategy from building single, lasting advantages to creating continuous sequences of differentiation.
Framework for AI Product Success
To build winning AI products in today's competitive environment, product teams must answer four fundamental questions:
1. What Are Your Unmet Customer Problems?
Identify customer needs that competitors are overlooking or addressing inadequately. Look beyond obvious pain points to discover desires for empowerment, assistance, or experiences that existing solutions don't provide.
2. What AI Capabilities Can Solve Those Problems in Novel Ways?
Determine which AI building blocks can address identified customer needs through unique approaches. Focus on combinations and applications rather than individual capabilities.
3. What Proprietary Data Can Power Those Solutions?
Assess what unique data you can leverage to provide AI models with distinctive context. Consider real-time information, user-specific patterns, domain expertise, and human judgment data that competitors lack.
4. What Superpowers Can Your Product Give to AI?
Identify how your existing functionality, workflows, integrations, and product capabilities can amplify AI effectiveness in ways competitors cannot replicate.
Building for Continuous Evolution
Success in today's AI landscape requires architecting products for continuous evolution rather than static competitive advantage. This means:
- Designing flywheel systems where initial differentiation creates data and capabilities for future enhancements
- Planning sequences of building blocks that can be assembled into new combinations as competitive pressures increase
- Maintaining execution velocity to stay ahead of the 2-3 week competitive response cycle
- Focusing on customer value rather than technological sophistication alone
Key Takeaways
The AI revolution has intensified competitive pressures across all technology categories, but it has also created opportunities for teams that approach differentiation strategically. Success requires moving beyond both custom AI infrastructure and basic feature copying to thoughtful assembly of AI capabilities with proprietary assets.
Your competitive advantage won't come from AI itself—it will emerge from how you combine AI capabilities with your unique data, functionality, and customer insights. The companies that thrive will be those that continuously sequence these building blocks into new forms of value creation.
In this knife-fight competitive environment, answering "what to build and why it will win" has become more challenging but also more critical than ever. The teams that master this strategic assembly will find their seams in even the most competitive landscapes.