AI R&D Landscape Project
Building a modern data-driven decision-support infrastructure for AI research and development
Building a modern data-driven decision-support infrastructure for AI research and development
Worldwide
- About usWe are an international think-and-do tank with 15+ years of experience in metascience, research management, science communication, and international higher education.
Our team members have a successful track record of planning and implementing comprehensive institutional development and strategy projects across the world, including providing consultancy to national ministries and governments in three countries. - About the projectThe project goal is to create a data-driven map of artificial intelligence research and development (R&D) across Europe and the world — identifying leading organisations and expertise areas, analysing talent and collaboration flows, and highlighting opportunities to establish high-impact R&D partnerships.
The project has multiple outcomes tailored to various audiences and use cases. These outcomes include thematic and regional reports, tailored briefings, research and general media publications, databases, and analytical solutions.
Why Work with Us
Why work with us
- Clear focus on AI R&D ecosystems: research groups, collaboration networks and talent pools
- Proven ability to translate complex research data into strategic decisions
- Deep understanding of how organizations identify partners, topics and innovation opportunities
- Experience working across academia, industry and public sector
Track record (selected):
- 30+ expert and research group searches delivered
- 25+ AI and innovation domains mapped and benchmarked
- Strategic analytics projects for leading universities, corporates and public institutions
Trusted by:
- Top-500 universities
- Companies with $10B+ revenue
- Public sector organizations in 5 countries
How stakeholders can benefit from the AI R&D Landscape data
For corporate leadership, university administration and policymakers
(01)
Targeted identification of research partners
Use data to find the world’s leading research groups and institutions in your specific field, rather than relying on personal networks or reputation
Use data to find the world’s leading research groups and institutions in your specific field, rather than relying on personal networks or reputation
(02)
Understand and leverage talent flows
Track where AI talent is migrating, where talent pools are forming, and where brain drain is occurring, to use as a basis for recruitment strategies and location decisions
Track where AI talent is migrating, where talent pools are forming, and where brain drain is occurring, to use as a basis for recruitment strategies and location decisions
(03)
Assess your competitive position
Objectively benchmark your organisation, region, or country against national and international competitors in terms of research output, research transfer, and collaboration density
Objectively benchmark your organisation, region, or country against national and international competitors in terms of research output, research transfer, and collaboration density
(04)
Map collaboration networks
Understand who is collaborating with whom, where strong clusters are emerging, and where bridges between academia and industry are missing
Understand who is collaborating with whom, where strong clusters are emerging, and where bridges between academia and industry are missing
(05)
Spot thematic trends early
Use keyword and topic analysis to see which research fields are gaining (or losing) momentum before these trends become widely apparent
Use keyword and topic analysis to see which research fields are gaining (or losing) momentum before these trends become widely apparent
(06)
Guide funding and investment based on evidence
Identify which AI subfields are growing, which are stagnating, and where gaps exist, informing decisions on resource allocation and funding programmes
Identify which AI subfields are growing, which are stagnating, and where gaps exist, informing decisions on resource allocation and funding programmes
For AI experts and researchers
(01)
Find potential collaboration partners
Use co-author network analysis to identify researchers working on complementary topics
Use co-author network analysis to identify researchers working on complementary topics
Why analyse AI research?
- A unique field of science that is rapidly transforming all other disciplines as well as humanity in general
- The start of the pathway to AI implementation (papers→models, libraries, datasets, repositories)
- Papers at these venues are symbolic capital signals at the centre of global competition for top talent and AI research evaluation
- Top venues are targeted by top businesses around the world, not only universities
- The competition is fierce, and the scale is huge, with blue chip corporations, universities and small startups targeting tens of thousands of top talents
A unique, rapidly developing field of research in which the business sector is represented just as actively as universities and research institutes
New framework to measure AI research
- -1-Rapidly evolving research landscape
AI research develops rapidly, and traditional research databases such as Web of Science, Scopus and OpenAlex may not consistently capture the latest developments at leading AI conferences. - -2-Growing importance of research intelligence
As AI transforms research and industry, reliable information on who is conducting leading AI research becomes increasingly important for businesses, universities, governments and other stakeholders. - -3-Need for reliable and consistent data
Counting publications at leading AI conferences requires consistent methods. Incomplete data and inconsistent approaches can lead to misleading comparisons across countries and organisations.
How do we analyse AI research?
(01)
Focus on top level AI conferences and journals
How do we define the top level AI venue?
We do not develop our own classification of leading AI conferences and journals: instead, we examine which top level AI conferences and journals are most frequently cited in job postings from tech companies.
For example, data coming from four public applicant-tracking APIs (Greenhouse, Ashby, Lever, Workable) for 90 AI companies, 7,694 postings first published in 2025 or 2026, shows that NeurIPS, ICML, and ICLR are the conferences most frequently mentioned in job postings from tech companies.
(02)
Traditional data infrastructure
The existing traditional infrastructure supporting data-driven decision-making in research and development took shape long before the AI boom and does not align with the realities of the rapidly evolving AI landscape.
For instance, organisational profiles and their sets were created for traditional scientific fields dominated by universities, but not for AI research and development.
Our modern data infrastructure
- More up-to-date data: indexing immediately following major AI conferences
- A more comprehensive database of organisations: AI startups, labs, etc.
- Higher accuracy of entity linking and data completeness
What makes our approach relevant
Most of the analytical tools currently available for analysing AI research:
- Aggregate large volumes of unfiltered publication data
- Treat all papers and conference tracks as equal, without quality control
- Offer limited filtering by organisation type or AI domain
- Usually lag behind major conferences and struggle to properly index their output
- Rely solely on affiliations reported in papers, often struggling with company profiles, especially startups
- Fail to capture career histories, education, or positions
Our approach:
- Focus on high-quality AI research: main tracks of curated venues
- Original paper sources, indexed as soon as they are published, with at least 3 major updates each year
- Self-reported positions capture 2-3x more career moves and include educational histories, updated bi-weekly
- Field-aware, frontier-AI-assisted classification and labelling of areas, data modalities, and approaches
- Granular filters by country and industry
- Curated database of organisations of all types with support for org lineages (mergers, national academies etc.)
- Collaboration and co-authorship networks analysis
- Modern semantic search/RAG finds you the right experts aligned with your specific needs
What does this achieve?
A higher signal-to-noise ratio for strategic AI development decisions
A higher signal-to-noise ratio for strategic AI development decisions
Connecting AI research to the real world
AI papers matter, but models do the real work
That’s why we capture and analyse Hugging Face downloads
That’s why we capture and analyse Hugging Face downloads
- Democratic but concentrated area: top 1.000 model suppliers (out of millions) account for >95% of downloads
- Downloads can be considered as a valuable proxy for measuring impact
- We link the downloads to real-world organizations and individuals and match them to countries
- We analyse the intended usage types for models, which enables us to identify the leaders in each field and the most rapidly developing use cases
Connection between top-level AI R&D and the real world
A case of the rapid growth of the sentence transformers ecosystem of models and tools
- Developed by Iryna Gurevich, the most distinguished German NLP researcher, and her team at TU Darmstadt
- Crucial for NLP (semantic search etc.)
- Currently maintained by Hugging Face company itself
- Account for >80% of Germany’s monthly model downloads
- Other German leaders by model downloads are companies, with universities trailing behind
How is cutting-edge research translated into the real world at a personal level?
Notably, one of Iryna Gurevich’s team leaders, Nils Reimers, became VP of AI Search at Cohere
Notably, one of Iryna Gurevich’s team leaders, Nils Reimers, became VP of AI Search at Cohere
Explore the global AI research landscape with r-d.ai
A unique analytics platform connecting AI researchers, organisations, expertise and talent flows - live since September 2026
How it works
Locate and track emerging topics, leading researchers and organisations, research trends, and global talent flows.
A unified analytics platform built on OpenReview and ROR data, extensively curated, enriched, and linked using decades of expertise in research metadata and metascience. >88k researchers, >8k hand-curated and matched organisation profiles, >232k career positions. Fully transparent and traceable up to paper level.
AI researchers & expertise
Explore AI researchers, their
expertise and career paths
Explore AI researchers, their
expertise and career paths
Organisations & countries
Benchmark AI research performance
Benchmark AI research performance
Mobility & collaboration
Survey talent flows and find collaboration partners
Survey talent flows and find collaboration partners
AI topics
Prospect emerging AI research
topics and areas of expertise
Prospect emerging AI research
topics and areas of expertise
ICLR 2021–2026, ICML 2023–2026, NeurIPS 2021–2025
Why it matters
Enabling faster, better-informed decisions on research priorities, funding strategies, partnerships, and talent attraction
What does this look like in practice?
For universities, research organisations, and enterprises
For universities, research organisations, and enterprises
For AI ecosystems & hubs
Explore where AI research talent is concentrated to support ecosystem positioning and engagement with partners and policymakers
Explore where AI research talent is concentrated to support ecosystem positioning and engagement with partners and policymakers
For public sector & policymakers
Compare AI research activity across countries and regions and analyse international talent flows to inform research and talent policies
Compare AI research activity across countries and regions and analyse international talent flows to inform research and talent policies
For HR professionals and talent scouting / expert search
We collect authors’ own keywords and add 4-aspect classification (area, modality, contribution type, and concerns) compared with the average to highlight specialisation
Detailed career histories linked to organisations for easy analysis
Use any query in any language and get full expert search results, grounded in real papers, scoped to your needs and optionally blending your internal data with our rich databases.
We also offer expert search for any research and innovation scopes besides top ML talent. For example, we can focus on complete coverage of Computer Science and Robotics in Baden Wurttemberg, and semantically link >40,000 academic experts there to local startups, regional priorities and EU innovation calls.
CONTACT US
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