New research explores how AI systems make money, value data and power agent-driven ecosystems
Anaxi Labs, an AI data infrastructure company with a new economic layer for next-generation AI systems, today announced a new research collaboration with Carnegie Mellon University (CMU) to explore one of the most critical and unresolved questions in artificial intelligence: how value is created, measured and distributed across emerging AI ecosystems. The collaboration brings together researchers from CMU and industry experts at Anaxi Labs to study the economic foundations of generative AI systems, with an initial focus on two areas shaping the next phase of AI development – agent-to-agent interaction and the valuation and pricing of datasets.
“AI is becoming less like a single product and more like an ecosystem,” said Kate Shen, co-founder of Anaxi Labs. “Agents call other agents, models rely on curated datasets– each output is increasingly the result of works of many contributors. The systems behind this shift need economic models that fairly evaluate and reward those contributions.”
As part of the partnership, Anaxi Labs and CMU are releasing their first joint research paper, “An Economic Framework for Generative Engines: Advertising or Subscription,” which examines how AI platforms can sustainably generate revenue as generative systems increasingly replace traditional search and discovery models.
Generative AI systems that deliver direct answers instead of links are reshaping the economic structure of the internet. While this shift improves speed and usability, it disrupts the advertising-driven model that has historically funded digital platforms.
The Anaxi Labs-CMU joint research explores how platforms can move beyond binary monetization strategies and adopt flexible frameworks that balance advertising and subscription models as systems evolve, as well as how to measure and compensate for the value of data.
“Modern AI systems rely on enormous volumes of training data, but the value of that data is rarely clear,” said Chenyan Xiong, associate professor at CMU’s Language Technologies Institute who is leading the research collaboration. “If we can estimate how much a dataset contributes to model performance, we can begin to understand what that data is actually worth.”
Earlier research by Prof. Xiong demonstrated that compensating data contributors based on the measurable value of their work directly results in higher-quality AI models. Anaxi Labs’ research collaboration with CMU plans to expand this research, driven by the conviction that the highest quality of output from the AI systems can only be generated from the highest quality of input (data and creativity), by experts who are motivated enough to view their contribution as a partnership instead of a transactional relationship.
This research has significant implications for fairness and incentives across the AI ecosystem, and aligns directly with Anaxi Labs’ broader mission to build a global data supply chain for AI and robotics, supported by a programmable marketplace for AI capabilities.
As AI increasingly becomes the primary interface for knowledge, software and decision-making, the incentives embedded in these systems will play a defining role in determining how value is distributed across the ecosystem. Anaxi Labs’ collaboration with CMU is designed to address these challenges early, before economic models become entrenched.
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