Nexbax
— The Next Billion AI IndexThe compass for AI utility and adoption in the global majority.
The Next Billion AI Index (nexbax) addresses the measurement blind spot in next billion markets, in which AI progress cannot be equated with benchmark supremacy. The index evaluates generative AI systems—including models and agentic architectures—not by abstract performance metrics alone, but by their capacity to deliver economically sustainable, inclusive, and developmentally relevant value across constrained environments.
Inspired by the precedent set by Stanford's Foundation Model Transparency Index, nexbax extends that momentum toward inclusivity, accessibility, and innovation for the next billion users, asking not only how powerful systems are, but how meaningfully they serve the global majority. Drawing on Prahalad's Bottom of the Pyramid principles, nexbax reinterprets them for the generative AI era through 10 dimensions organised under 3 themes: Effective Efficiency, Operational Practicality, and Societal Integrity.
With clear rubrics and comparative evaluations spanning model families, orchestration frameworks, and vertical solutions, nexbax enables benchmarking that goes beyond performance—rewarding technologies that are open, resilient, and socially aligned.
Formative Study
A formative evaluation of nexbax with eleven experts provided early signal on its efficacy in capturing deployment considerations and its usefulness for practitioners.
Full details can be found in our white paper.
What we did
We conducted an evaluation of nexbax with eleven subject matter experts working on AI products for next billion markets, including founders, developers, and product leaders across India, Kenya, and Ghana. Over the course of one-hour interviews, participants applied the nexbax dimensions to three common AI configurations: open-weight models, closed models, and model-plus-orchestration systems. The goal was to assess whether the index was clear, useful, and accurate from the perspective of practitioners who build and deploy AI in these contexts.
What we found
Experts broadly felt the index captured the factors that matter most in their work:
"All the tools that we use to evaluate our customers beyond localization, you hit all of them, ease of use, reliability, the cost, the set up time. Those are the same types of things that we use to pick our vendors."
The three most important dimensions for this group were cost effectiveness, usability & automation, and trustworthiness & ethics, spanning all three nexbax themes. This provides early evidence that each theme captures a distinct and meaningful aspect of real-world decision-making.
Participants also saw practical value in having a shared evaluation framework. One described it as an "elimination mechanism" for ruling out technologies that score poorly across dimensions; another said it "would help me choose the right models to use." A developer reflected on the absence of community consensus today:
"Within the community, there's no consensus on what works for us...the index would help us as a community figure out what's best for us, beyond just building policy documents."
What we've improved
Participants noted that numerical ratings alone can obscure important nuance. For example, a cost effectiveness score carries different implications depending on whether the focus is upfront setup costs or long-term pricing. In response, future evaluations will include written explanations alongside ratings, and will specify the domain and deployment context to help readers assess relevance to their own situation.
On usability, most participants found the rubrics straightforward to apply, though some noted they benefited from researcher guidance during the session. We have since added clearer definitions for each dimension, broader examples covering a wider range of use cases, and renamed dimensions where terminology caused confusion. For instance, "Robustness (Resilience and Reliability)" was renamed to "Resilience, Reliability, and Robustness" after several participants interpreted robustness as referring to output accuracy.
Next Steps
We are continuing to improve nexbax and welcome your feedback.
Acknowledgments
We thank Varun Aggarwal, Anthony Annunziata, Ginette Azcona, Daksh Chawla, Nahuel Defosse, Giridhar Ganapavarapu, George K. Githae, Sudeep Gowrishankar, Raghav Gupta, Jaikrishnan Hari, Ronak Khandelwal, Jordan McAfoose, Rohan Sahu, Siddhant Sachdeva, Het Shah, and Amith Singhee for their assistance with the survey process, feedback, and other contributions during the development of this work. Subhabrata Majumdar's research is supported by the Indian Institute of Management Bangalore Young Faculty Research Grant.
If you use nexbax in your work, please cite our white paper:
@misc{rawat2026nexbax,
title = {Next-Billion {AI} Index: The compass for {AI} utility and adoption in the global majority},
author = {Rawat, Ambrish and He, Jessica and Majumdar, Subhabrata and
Pinhanez, Claudio and {Le Beux}, Yann and Krishna, Satyapriya and
Gupta, Rahul and Chowdhury, Rumman and Varshney, Kush R.},
year = {2026},
month = may,
eprint = {2606.00359},
archivePrefix = {arXiv},
primaryClass = {cs.CY},
doi = {10.48550/arXiv.2606.00359}
}
Rawat, A., He, J., Majumdar, S., Pinhanez, C., Le Beux, Y., Krishna, S., Gupta, R., Chowdhury, R., & Varshney, K. R. (2026). Next-Billion AI Index: The compass for AI utility and adoption in the global majority. arXiv. https://doi.org/10.48550/arXiv.2606.00359
Rawat, Ambrish, et al. "Next-Billion AI Index: The Compass for AI Utility and Adoption in the Global Majority." arXiv, 29 May 2026, arxiv.org/abs/2606.00359.