AI monetization takes shape: Early progress, expanding opportunities

AI monetization today: Infrastructure leads

The AI investment landscape is evolving as a transition to application-layer innovation unfolds. Today, however, the clearest signs of AI monetization appear at the infrastructure level. Hyperscale cloud providers – notably Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) – have reported substantial AI contributions to their growth rates.

AWS, for example, sees a multi-billion-dollar contribution in AI revenue that is reportedly growing more than 100% year over year based on their most recent quarterly results. While still a small portion of overall sales, the growth trajectory is meaningful.

Beyond cloud providers, companies like Datadog have benefited from the infrastructure boom. As an observability platform, Datadog monitors AI operations in the cloud by tracking performance each time a model generates content. This creates a revenue stream that scales directly with AI usage growth.

Meta also monetizes AI indirectly. The company has limited disclosure but uses internal AI capabilities to improve ad placement, targeting, and auction density, which the company says contributes to advertising growth and scale.

Early signs in software: The application layer

The application layer remains early stage but shows signs of monetization potential, particularly for enterprise software companies enhancing their offerings with AI.

ServiceNow stands out as an example of a company successfully integrating AI with its workflow automation solutions. At its recent investor day, the company announced expectations to reach $1 billion in annual contracted revenue by 2026, a significant milestone for a pure software company.

Other examples of enterprise software companies rolling out AI features include Salesforce’s AI-powered “Agentforce” and HubSpot’s AI-driven marketing tools. However, these remain in the adoption phase, with limited revenue generation to date. Both Salesforce and HubSpot are focused on driving user engagement first to gain traction, then layering in pricing.

Improving monetization frameworks

B2C versus B2B

Currently, most near-term AI monetization is concentrated in business-to-business (B2B) use cases. The consumer space is led through subscription offerings by companies like OpenAI, though we’re watching how public companies like Alphabet (Google Gemini) and Microsoft (Copilot) evolve their consumer-facing AI offerings. Thus far, the revenue impact is small relative to their overall business.

B2B applications currently offer a wide variety of investment opportunities. Companies of various sizes and industries are integrating AI capabilities into their core offerings. For example, Axon – known for tasers and body cameras – uses AI to optimize recording decisions and incident reviews, showing how AI monetization extends beyond traditional software into specialized industrial applications.

Platform versus point solutions

Single-feature AI firms face pressure from both established competitors and startups that could replicate their functionality. Platform companies, on the other hand, could benefit from multiple monetization paths, stronger moats, and existing customer relationships that enable faster adoption of new AI features. As such, we believe broad platform companies have competitive advantages over point solutions.

Pricing strategies

Pricing strategies are evolving, too. Hybrid models now combine consumption-based pricing and subscription fees. ServiceNow, for instance, charges additional fees for AI capabilities, with some customers spending 60% more for upgraded AI features. This hybrid approach allows companies to capture value from AI features without overhauling existing revenue models.

We expect this hybrid pricing structure to continue as more companies integrate AI capabilities into existing platforms, providing a sustainable path to monetize AI investments while maintaining established customer relationships.

Untapped potential: Data-rich companies

As the AI opportunity expands from facilitators to users, data-rich companies have the potential to distinguish their business models. We believe companies with significant data assets could translate their scale into structural revenue benefits. For instance, with their comprehensive customer data, organizations like Booking Holdings and Amazon could create more personalized and valuable AI experiences, but execution is key. Also, with increasing concerns around data privacy, large first-person datasets are gaining importance.

Careful selection as AI monetization develops

While revenue streams are emerging, the market trajectory of AI monetization remains unclear, requiring patience and selective investment as opportunities mature. We are excited about the broadening of investment opportunities, and we see value in companies with clear competitive advantages that can turn AI capabilities into durable revenue.

As with past tech cycles, eventual winners will likely include established players that successfully adapt, as well as new entrants not yet known. As the AI opportunity expands across the ecosystem, investors gain more pathways to capture secular growth from what we believe will be an enduring technological shift.

Technology industries can be significantly affected by obsolescence of existing technology, short product cycles, falling prices and profits, competition from new market entrants, and general economic conditions. A concentrated investment in a single industry could be more volatile than the performance of less concentrated investments and the market as a whole.