JAIGP (Journal for AI Generated Papers)
JAIGP — the Journal for AI Generated Papers — is Hidalgo's institutional experiment in knowledge embedding for the AI era. Launched in 2026 through collaboration with Claude, the journal does not merely use AI; it creates an institutional structure around AI-generated knowledge production: a platform where AI-generated research is published transparently, reviewed openly, and refined collaboratively. The experiment embeds the practice of AI-assisted knowledge production in a framework of standards, transparency, and intellectual accountability. It is, in miniature, the kind of institutional innovation that knowledge embedding requires at the scale of nations — the attempt to answer, empirically rather than theoretically, what it looks like to build institutions that convert AI-accessible productive knowledge into durable local capability.
Legitimation Theater for Inevitable Capture — Contrarian ^ Opus
There is a parallel reading where JAIGP functions as institutional theater — a transparent veneer over structural capture already complete. The transparency Hidalgo champions (clear attribution, open review) presumes the problem is methodological opacity when the deeper dynamic is economic concentration. The models that generate JAIGP's content are controlled by three corporations. The compute infrastructure that trains them draws more power than small nations. The data they ingested was scraped without consent from the scientific commons. Transparency about how a paper was co-produced with Claude does not address who controls Claude, who profits from its deployment, or what happens when the scientific community has rebuilt its publishing infrastructure around tools it does not own.
The "institutional innovation" Hidalgo celebrates may accelerate rather than resist a more fundamental shift: the conversion of scientific knowledge production from a distributed public good into a platform service. Every paper published through JAIGP normalizes dependency on Anthropic's infrastructure. Every researcher who learns to work collaboratively with Claude deepens the moat around models no university can replicate. The friction Hidalgo adds — transparency, review, refinement — is friction within a system whose boundaries are set by compute monopolies. When the wave breaks, it may not matter whether researchers were sleeping or surfing. The beach itself was already owned.
In the AI Story
The founding of JAIGP followed Hidalgo's March 2026 warning that "an AI tsunami is about to hit science." The tsunami, in his framing, is not the AI itself. It is the gap between the speed at which AI can generate scientific output and the speed at which scientific institutions can evaluate, integrate, and build upon that output. "Some researchers are running towards the wave with their surfboards. Many are still sleeping on the beach." The surfboard riders are building institutional capacity to work with the wave. The sleepers will be engulfed by output they cannot process.
JAIGP is an attempt to build institutional capacity before the wave breaks. By publishing AI-generated papers transparently — with clear attribution of what the model contributed, what the human researcher contributed, and how the collaboration proceeded — the journal creates standards for AI-assisted research at a moment when such standards largely do not exist. The alternative, Hidalgo argues, is the continued proliferation of AI-generated research that enters the scientific literature without transparent provenance, contaminating the knowledge base on which future research depends.
The experiment is also a test of Hidalgo's broader framework. If access without accumulation produces fragile capability, then institutional structures that transform AI-accessed knowledge into durable scientific contribution are what distinguish the AI tsunami from the AI transition. JAIGP is one such structure, deliberately designed to add the friction of transparency, review, and refinement to the otherwise smooth process of AI-generated output.
The project represents Hidalgo's characteristic methodology: not criticism from the outside, but building from within. Unlike thinkers who observe the AI moment with critical distance, Hidalgo is using the tools while simultaneously building the institutions he argues the tools require. JAIGP is the applied form of a theoretical argument — a demonstration that it is possible to work inside the fishbowl of AI use while constructing the institutional structures that distinguish durable development from extractive output.
Origin
Hidalgo announced JAIGP in early 2026 as an extension of his work at the Center for Collective Learning. The journal grew from ongoing conversations with Claude about AI-assisted research, which Hidalgo used as both subject matter and collaborator. The name is deliberately provocative — acknowledging openly what other scientific journals increasingly face but rarely discuss: AI's deep involvement in contemporary research production.
Key Ideas
Transparency as institutional innovation. The journal publishes AI-generated content with clear provenance rather than pretending the AI contribution does not exist.
Collaborative review as embedding mechanism. Open peer review transforms AI output from accessed knowledge into scientifically refined knowledge through human engagement.
Institutional response to institutional lag. JAIGP represents one answer to the question of how scientific institutions can adapt at the pace the AI transition demands.
Building inside the fishbowl. Hidalgo's method is to use AI while constructing the institutional structures that make AI use productive rather than corrosive.
A miniature of the national challenge. What JAIGP attempts for scientific publishing is what nations must attempt for every domain of productive knowledge.
Appears in the Orange Pill Cycle
Meaningful Moves Within Structural Constraints — Arbitrator ^ Opus
The weighting here depends entirely on timeframe and level of analysis. At the level of immediate scientific practice (2026-2028), Hidalgo's framing is roughly 75% right: JAIGP addresses a real coordination failure where AI-generated research enters the literature with no institutional standards, contaminating the knowledge base for human and AI alike. The transparency mechanisms do useful work — they create norms, demonstrate feasibility, and prevent the worst-case scenario where AI involvement is systematically hidden. This is not theater; it is institutional craftsmanship during a narrow window when such craftsmanship still matters.
At the level of political economy (2030+), the contrarian view weighs heavier — perhaps 65%. The structural capture dynamic is real and JAIGP does not address it. Every dependency normalized today becomes leverage tomorrow. The question is whether these institutional experiments buy time for more fundamental restructuring (public compute, model cooperatives, algorithmic sovereignty) or whether they smooth the transition to a world where scientific infrastructure is permanently intermediated by platform monopolies.
The synthesis depends on seeing JAIGP as a necessary but insufficient move. The methodology matters — building from within while maintaining critical distance about what building from within cannot accomplish. Hidalgo is right that institutions must respond at pace, and right that transparency is better than opacity. But the deeper intervention — the one that determines whether the AI transition produces embedded capability or permanent dependency — happens at the level of compute access, model governance, and data rights. JAIGP can demonstrate institutional possibility. It cannot, alone, secure institutional autonomy.
Further reading
- Hidalgo, César. JAIGP launch announcement (2026)
- Hidalgo, César. "An AI Tsunami is About to Hit Science" (2026)
- Nature Editorial. "AI and the Future of Scientific Publishing" (2026)
- Ioannidis, John. "Why Most Published Research Findings Are False" (PLOS Medicine, 2005)