Airbus for AI: A global strategy for public value creation
Bibliographic record
Abstract
Artificial intelligence (AI) is becoming central to economic productivity and national sovereignty, but its current frontier development is concentrated in a few dominant U.S. and Chinese firms. Many middle power countries like Canada, Germany, Japan, Spain, Sweden, Switzerland, and the U.K. have struggled to field competitive AI products despite substantial public and private investment across national labs and national champions (Allen, 2025; Council of the European Union, 2023; Escritt, 2023; Kreps, 2024; Scott, 2024; Stage et al., 2024; Stanford HAI, 2024). Insufficient funding, a talent/compensation gap, regulatory constraints, and fragmented markets all hinder their ability to scale AI initiatives effectively. As a result, directors in several national AI labs have called for more coordinated public interventions (Bengio, 2023; Valero & Crespo, 2024) while policymakers are debating the case for industrial policy interventions to support domestic AI firms (AI Now Institute, 2024; European Commission, 2025b; UK Government, 2025). If AI is to serve the economic and security interests of all countries, it must be developed in a way that balances public value generation with a credible business model and a pathway to scale in global markets. Any solution must also respond to emerging industrial trends in AI, including the increasing commodification of large language models (LLMs), the rise of open source, and the dramatically increasing salience of national security and geopolitics to trade in AI. There are precedents for such solutions. In particular, Airbus, originally developed by a set of middle powers to compete in aerospace, provides a successful example of an international public-private collaboration. This policy brief proposes a Public AI Company inspired by such models—a coordinated public-private partnership that ensures AI development serves shared national interests and maximizes public value rather than concentrating benefits in a handful of private entities headquartered in global superpowers. Drawing on decades of economic and public policy research as well as a technical analysis of current AI supply chains, this brief outlines why such a global approach is both necessary and achievable, and discusses some of the risks and challenges involved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.059 | 0.027 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".