Startups can compete with large AI labs by capturing unique user interaction data from specialized workflows. This proprietary "user signal" enables post-training of models for specific tasks, creating a defensible advantage that labs, lacking that specific context, cannot easily replicate.

The notion of building a business as a 'thin wrapper' around a foundational model like GPT is flawed. Truly defensible AI products, like Cursor, build numerous specific, fine-tuned models to deeply understand a user's domain. This creates a data and performance moat that a generic model cannot easily replicate, much like Salesforce was more than just a 'thin wrapper' on a database.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

Canva avoids competing with giants like OpenAI on foundational models. Instead, it partners with them for general tasks while focusing its 100-person research team on specialized models for core design problems, like its 'Magic Layers' feature, where no adequate external solution exists.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

As AI application layers become easier to clone, the sustainable competitive advantage is moving down the tech stack. Companies with unique, last-mile user interaction data can build proprietary models that are cheaper and better, creating a data flywheel and a moat that is difficult for competitors to replicate.

The ability for Canva's AI to orchestrate complex designs across documents, presentations, and videos wasn't a recent development. It was built on a decade of investment in a single, flexible design format, which provided the necessary architectural foundation for a design-focused foundational model.

As AI makes building software features trivial, the sustainable competitive advantage shifts to data. A true data moat uses proprietary customer interaction data to train AI models, creating a feedback loop that continuously improves the product faster than competitors.

To navigate the AI shift, Canva built its own unique IP with an in-house team. This allowed them to move faster with decentralized "speed boats," returning to a startup-like product cadence despite their large size, rather than being beholden to external models.