Contrary to conventional wisdom, MongoDB's CEO reveals enterprise leaders have a surprising appetite for full system replacement. An AI-native company that can replace an entire legacy system of record—making it cheaper, faster, and better—will get a leader's attention far more effectively than one offering an incremental feature layer on top of an existing platform.

The most successful AI applications like ChatGPT are built ground-up. Incumbents trying to retrofit AI into existing products (e.g., Alexa Plus) are handicapped by their legacy architecture and success, a classic innovator's dilemma. True disruption requires a native approach.

Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

Notion's CEO compares current AI adoption to swapping a water wheel for a steam engine but keeping the factory layout the same. The real gains will come from fundamentally rethinking workflows, meetings, and hierarchies to leverage AI that works 24/7, rather than just layering it onto existing processes.

Sam Altman believes incumbents who just add AI features to existing products (like search or messaging) will lose to new, AI-native products. He argues true value comes not from summarizing messages, but from creating proactive agents that fundamentally change user workflows from the ground up.

For incumbent software companies, surviving the AI era requires more than superficial changes. They must aggressively reimagine their core product with AI—not just add chatbots—and overhaul back-end operations to match the efficiency of AI-native firms. It's a fundamental "adapt or die" moment.

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.

Adding AI tools to current processes yields only incremental efficiency. To achieve significant business impact, leaders must rebuild their entire go-to-market system—roles, workflows, and data flow—with AI at the core, not as an add-on.

Don't just plug AI into your current processes, as this often creates more complexity and inefficiency. The correct approach is to discard existing workflows and redesign them from the ground up, based on the new paradigms AI introduces, like skipping a product requirements document entirely.