AI business models that actually work

In a landscape where everyone's talking about AI but few are making money from it, Alvaro Morales of Orb offers a refreshingly practical perspective on sustainable AI monetization strategies. While venture capital continues pouring billions into AI startups, the question remains: how do you build an AI business that generates actual revenue? This isn't just about technological innovation—it's about finding business models that create genuine value for customers willing to pay for it.

Key Points

• AI adoption follows a classic diffusion curve – We're still in the early adopter phase for enterprise AI, with many businesses experimenting but not yet integrating AI deeply into operations. The technology needs to cross the chasm to reach mainstream adoption.

• Three proven AI business models have emerged – These include: AI-powered apps with unique workflows, AI agents automating specific tasks, and infrastructure plays that provide essential tools for AI implementation.

• Market timing is crucial for success – Different segments of the AI market (infrastructure, agents, applications) are maturing at different rates, requiring strategic alignment of business models with market readiness.

• Regulatory landscapes significantly impact viability – Industries with high regulatory scrutiny create both barriers to entry and opportunities for specialized AI solutions that can navigate compliance requirements.

Why the "Three Models" Framework Matters

The most compelling insight from Morales's analysis is his three-model framework for AI monetization. This matters tremendously because it cuts through the hype to provide a practical roadmap for entrepreneurs and businesses.

Unlike the generalized excitement about AI's potential, this framework grounds the discussion in business fundamentals: creating value that customers will pay for. The infrastructure play focuses on providing essential tools and platforms other businesses need to implement AI. The agent model centers on automating specific workflows through AI. The application approach involves embedding AI capabilities into existing software to enhance functionality.

What makes this framework particularly valuable is its alignment with market maturity. Infrastructure plays are working now because they're essential to the entire ecosystem. Agent models are gaining traction as trust builds around specific use cases. Application models require more market education but offer tremendous scaling potential once adopted.

This strategic differentiation helps explain why some AI ventures succeed while others struggle despite having impressive technology. Success depends not just on technological innovation but on choosing a business model that matches current market