Application Layer for AI

In the rush to build AI infrastructure, we're seeing a gold rush mentality similar to the early days of cloud computing. While venture capital flows heavily into AI infrastructure plays - the "picks and shovels" of the AI revolution - there's a compelling case for why the real opportunity lies in the application layer. Drawing from my experience building infrastructure at scale, I've observed a fundamental shift in how we should approach AI development.

The Infrastructure Challenge

During my time at Databricks, I witnessed firsthand the complexities of managing infrastructure at scale. Our team handled control plane infrastructure across AWS, Azure, GCP, spanning public clouds, on-premise deployments, government clouds, and China regions. This experience provided unique insights into both the power and limitations of infrastructure solutions.

A critical observation from the field: when companies don't fully understand infrastructure, they often overinvest in solutions they don't need. This pattern is particularly evident in the AI space, where we're seeing companies dramatically increase prices due to costs and unhealthy margins.

Unlike traditional SaaS margins of 80-90%, AI services face fundamentally different economics:

• Demand growth outpaces cost reduction
• Infrastructure complexity increases with scale, compaines will charge some arbitrary amount for "platform fees" or "enterprise features"

This integration challenges the value proposition of specialized AI infrastructure startups. As one senior engineer at a major cloud provider noted, "Betting on an unknown startup for critical infrastructure is increasingly difficult to justify when established players offer similar capabilities with proven reliability." Why Applications Matter More The future of AI lies not in building better infrastructure, but in creating more effective applications. Here's why:

Market Readiness: End-users are looking for solutions, not infrastructure Economic Efficiency: Well-designed applications can achieve 80% of specialized infrastructure benefits at a fraction of the cost Faster Innovation: Application-layer development allows for rapid iteration and direct user feedback

Looking Ahead As we move forward, the successful AI companies won't be those building marginally better infrastructure tools, but those creating applications that solve real user problems. The infrastructure layer will continue to commoditize, while the real value creation happens at the application layer where user interaction occurs. This perspective isn't just theoretical - it's grounded in years of hands-on experience with both infrastructure and application development. The future belongs to companies that can bridge the gap between powerful AI capabilities and practical user needs, not those building incrementally better infrastructure tools.