In an AI-driven ecosystem, data and content need to be fluidly accessible to various systems and agents. Any SaaS platform that feels like a "walled garden," locking content away, will be rejected by power users. The winning platforms will prioritize open, interoperable access to user data.

Intentionally create open-ended, flexible products. Observe how power users "abuse" them for unintended purposes. This "latent demand" reveals valuable, pre-validated opportunities for new features or products, as seen with Facebook's Marketplace and Dating features.

To serve both solo developers and large enterprises, GitHub focuses on creating horizontal "primitives" and APIs first. This foundational layer allows different user types to build their own specific workflows on top, avoiding the trap of creating a one-size-fits-none user experience.

The traditional competitor for B2B tools was an Excel spreadsheet. In the AI era, it's a simple, version-controlled Markdown file within an IDE. If a SaaS offering for documentation or project management can't provide more value than this highly flexible, interoperable setup, it will lose.

Contrary to fears of chaos, allowing users to modify their software can create more stability. Users can craft a predictable, long-lasting environment tailored to their needs. This control protects them from disruptive, top-down redesigns pushed by a distant corporate office.

Products are no longer 'done' upon shipping. They are dynamic systems that continuously evolve based on data inputs and feedback loops. This requires a shift in mindset from building a finished object to nurturing a living, breathing system with its own 'metabolism of data'.

The surprising success of Dia's custom "Skills" feature revealed a huge user demand for personalized tools. This suggests a key value of AI is enabling non-technical users to build "handmade software" for their specific, just-in-time needs, moving beyond one-size-fits-all applications.

In the future, it will be easier for businesses to build their own custom software (e.g., Salesforce) through prompting than to buy and configure an off-the-shelf solution. This shift towards "liquid software" will fundamentally challenge the one-size-fits-all SaaS model, especially for companies that currently rely on implementation partners.

Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.