Don't view AI as just a feature set. Instead, treat "intelligence" as a fundamental new building block for software, on par with established primitives like databases or APIs. When conceptualizing any new product, assume this intelligence layer is a non-negotiable part of the technology stack to solve user problems effectively.

Instead of merely 'sprinkling' AI into existing systems for marginal gains, the transformative approach is to build an AI co-pilot that anticipates and automates a user's entire workflow. This turns the individual, not the software, into the platform, fundamentally changing their operational capacity.

Unlike co-pilots that assist developers, Factory's “droids” are designed to be autonomous. This reframes the developer's job from writing code to mastering delegation—clearly defining tasks and success criteria for an AI agent to execute independently.

Because AI agents operate autonomously, developers can now code collaboratively while on calls. They can brainstorm, kick off a feature build, and have it ready for production by the end of the meeting, transforming coding from a solo, heads-down activity to a social one.

For years, Google has integrated AI as features into existing products like Gmail. Its new "Antigravity" IDE represents a strategic pivot to building applications from the ground up around an "agent-first" principle. This suggests a future where AI is the core foundation of a product, not just an add-on.

A truly "AI-native" product isn't one with AI features tacked on. Its core user experience originates from an AI interaction, like a natural language prompt that generates a structured output. The product is fundamentally built around the capabilities of the underlying models, making AI the primary value driver.

The future of software isn't just AI-powered features. It's a fundamental shift from tools that assist humans to autonomous agents that perform tasks. Human roles will evolve from *doing* the work to *orchestrating* thousands of these agents.

Traditionally, building software required deep knowledge of many complex layers and team handoffs. AI agents change this paradigm. A creator can now provide a vague idea and receive a 60-70% complete, working artifact, dramatically shortening the iteration cycle from months to minutes and bypassing initial complexities.

The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.