Snowflake boosted revenue with AI not through internal productivity gains, but by embedding AI capabilities directly into its core analytics product. This made the platform more valuable and easier for customers to use, which in a consumption-based model, directly drove more usage and revenue.

The battle for enterprise AI is being fought on two fronts. Data platforms like Snowflake build agents from a governed data foundation (Data+AI), while model companies like OpenAI push general agents down into enterprise systems (AI+Data). The winner controls the core workflow.

Value in the AI stack will concentrate at the infrastructure layer (e.g., chips) and the horizontal application layer. The "middle layer" of vertical SaaS companies, whose value is primarily encoded business logic, is at risk of being commoditized by powerful, general AI agents.

Contrary to the "SaaS-pocalypse" theory, AI agents will become a new, high-volume user base for SaaS tools. This will drive massive growth for companies that adapt their products to be usable by both humans and AI agents simultaneously.

Sreedhar Ramaswamy believes AI agents represent the "industrialization of software," fundamentally altering its economics. This makes AI model companies a greater long-term competitor than traditional cloud giants, as they are becoming the new "front door" to computing and information, threatening all software companies, including Snowflake.

Snowflake's CEO warns that traditional software firms with walled-garden data models are vulnerable. If they don't develop their own compelling agentic interfaces, they risk being reduced to mere data sources for dominant AI platforms, losing their customer relationship and pricing power.

Snowflake moved beyond basic AI tools by building proprietary agentic models. One agent analyzes campaign data in real-time to optimize ad spend and ROI. A second 'competing agent' provides on-demand talking points for sales and marketing to use against specific competitors, solving a massive enablement challenge.

Contrary to the 'SaaSpocalypse' narrative, Jensen Huang believes AI agents will use existing SaaS tools rather than replace them. This will increase demand for best-in-class software like databases, as it's more efficient for an agent to leverage an existing tool than to build one from scratch.

The key differentiator for SaaS companies is being "in the token flow," where AI model usage directly drives consumption of their product (e.g., more database queries). Companies outside this flow, like some front-facing apps, risk competing directly with AI models and face significant headwinds.