Impact Analytics
Measuring how AI research translates into real-world products. These metrics go beyond citations — they track actual product adoption.
15
Researchers
12
Builders
13
Papers
13
Projects
26
Paper→Product Links
76.9%
Translation Rate
Research Impact Score Leaderboard
Researchers ranked by real-world product adoption
Ilya Sutskever
Safe Superintelligence Inc.
Ashish Vaswani
Essential AI
Noam Shazeer
Fei-Fei Li
Stanford University
Yoshua Bengio
Mila / Université de Montréal
Ian Goodfellow
Google DeepMind
Yann LeCun
Meta AI / NYU
Most Adopted Papers
Papers ranked by number of products built from them
Attention Is All You Need
NeurIPS 2017 · 2017 · 120,000 citations
products
Language Models are Few-Shot Learners
NeurIPS 2020 · 2020 · 35,000 citations
products
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
NeurIPS 2022 · 2022 · 5,200 citations
products
Liquid Time-constant Networks
AAAI 2021 · 2021 · 850 citations
products
Generative Adversarial Nets
NeurIPS 2014 · 2014 · 65,000 citations
products
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
ICLR 2019 · 2019 · 6,500 citations
products
Gradient-based learning applied to document recognition
Proceedings of the IEEE · 1998 · 45,000 citations
products
Learning Transferable Visual Models From Natural Language Supervision
ICML 2021 · 2021 · 22,000 citations
products
ImageNet: A Large-Scale Hierarchical Image Database
CVPR 2009 · 2009 · 50,000 citations
products
Training language models to follow instructions with human feedback
NeurIPS 2022 · 2022 · 8,500 citations
products
Research → Product Domain Flow
How research domains translate into product domains
Domain Breakdown
Research and product activity by domain
| Domain | Researchers | Papers | Projects |
|---|---|---|---|
| Generative AI | 10 | 8 | 12 |
| NLP | 4 | 5 | 7 |
| Computer Vision | 4 | 3 | 2 |
| AI Safety | 5 | 2 | 0 |
| Robotics | 2 | 1 | 3 |
| Reinforcement Learning | 3 | 1 | 0 |
| Healthcare AI | 2 | 1 | 0 |
| Optimization | 0 | 1 | 2 |
| Speech & Audio | 0 | 0 | 1 |
About These Metrics
The Research Impact Score (RIS) is a novel metric that measures real-world product adoption of academic research. Unlike citation-based metrics (h-index, impact factor) or attention-based metrics (Altmetric score), RIS tracks how many products and projects were actually built using a researcher's work. The score combines four components: Product Adoption Count (40%), Domain Breadth (30%), Foundation Index (20%), and Translation Rate (10%). The Research Translation Rate measures what percentage of papers have at least one real-world product — a metric no existing platform (Altmetric, PlumX, Overton, Dimensions, Lens.org, or Google Scholar) currently provides.