Anaxi Labs and Carnegie Mellon University launch research collaboration to shape the economics of AI

Today, we are announcing a new research collaboration with Carnegie Mellon University (CMU) to study the economic foundations of generative AI systems. We’re kicking off the partnership with a new whitepaper examining one of the industry’s most pressing questions: how AI engines should actually make money.

Alongside CMU, we will explore two areas likely to shape the next phase of AI development: agent-to-agent interaction, where specialized AI systems coordinate to complete complex tasks; and the evaluation and pricing of datasets, an increasingly important challenge as data becomes a central input to modern AI systems.

We are excited to release our first joint research paper, An Economic Framework for Generative Engines: Advertising or Subscription. The paper addresses a debate that has emerged across the AI industry as conversational systems increasingly replace traditional search results.

Generative engines now deliver answers directly instead of requiring users to click through multiple links. That shift improves speed and convenience for users while creating a new economic challenge for the companies operating these systems. Producing high-quality AI responses is computationally expensive, yet the advertising model that funded the web for decades relies on clicks that disappear when answers are generated directly.

The industry response has started to diverge. Some platforms are exploring ways to incorporate advertising into AI responses. Others, including Anthropic, have emphasized an ad-free experience built around subscription revenue.

Our research suggests the real answer is rarely absolute.

The next economic question for AI

The paper explores how generative AI platforms might navigate that tension. Rather than treating advertising and subscriptions as opposing strategies, it outlines a framework for understanding when different monetization approaches may make sense as AI systems evolve.

These questions connect directly to the broader problem Anaxi Labs wants to address. As model performance improves across the industry, the source of differentiation is moving beyond the model itself. Value increasingly comes from specialized knowledge and proprietary data embedded inside real workflows, much of which already exists across companies and research environments. What has been missing is infrastructure that allows those capabilities to be packaged, discovered, trusted, and compensated.

Underneath the global data supply chain for AI and robotics Anaxi Labs is building, the company is working on a programmable marketplace for AI capabilities, including prompts, agents, workflows, and datasets. Each component can be published as a reusable building block that others can integrate into larger systems. Usage is metered, and revenue is automatically shared with contributors whose assets power downstream outcomes.

This approach treats AI less like a single product and more like an ecosystem. Agents will call other agents, workflows will incorporate external tools, and models will rely on curated datasets maintained by independent contributors.

Why incentives will shape the future of AI

As that ecosystem develops, the central challenge becomes economic rather than purely technical. Platforms must decide which capabilities to surface, how contributors and their datasets are evaluated and priced, and how incentives shape the long-term health of the system. Our research collaboration with CMU is intended to explore those questions before the industry settles into fragile models.

If AI becomes the primary interface to knowledge, software, and decision-making, the systems behind it must reward the people who build useful capabilities and produce valuable data. Without those incentives, value risks concentrating in a narrow layer of model providers while discouraging the contributions that make AI systems useful in the first place.

Our partnership with CMU marks an early step toward developing the economic frameworks that will help the AI ecosystem grow sustainably as generative systems continue to reshape how information, software, and services are created and delivered.

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