Which AI economics and governance research most needs independent expert evaluation? (help The Unjournal prioritize it)

Published on October 20, 2026 6:39 PM GMT The Unjournal commissions public expert evaluations and ratings of research (and “pivotal questions”) with potential for global impact. We’re currently making a push to consider AI economics and governance research.[1] We’re seeking input from people who use this: which themes, which papers/projects, which claims, who should evaluate them, what should evaluators focus on, and why? (If you only have a moment, skip to “What would help most”.) Why do this? (Why us, and why now?) The usual story, which you’re ~probably on board with: AI is advancing rapidly, and becoming more clearly and deeply important to most major global priorities. It’s having large actual and predicted impact on the economy and the concentration of power. This is itself entangled with AI safety and AI global catastrophic risks. “How fast will capabilities grow and replace human work?” “How will gains flow to labor versus capital?” “How will revenue and compute accumulate in a few firms and countries?” “Will regulation change which models get released where?” Questions like these impact the nature of catastrophic AI-risk (resources, race dynamics, human empowerment, state capacity, etc.), as well as the strategic approaches to handling it. There are also direct implications for global poverty, global health systems, and for non-AI GCR (e.g., bioengineered pandemics).[2] These questions (and ‘cruxes’) seem to directly impact a set of live decisions surrounding AI safety strategy, philanthropic funding, regulation and legal safeguards, public spending, international controls, advocacy, and more. Economists, social scientists, and policy analysts have been moving into this area. Some of the work crosses disciplinary lines. Economists are now modeling AI scaling and recursive self-improvement. Growth theorists are co-authoring scenario models with funding and data from frontier labs. Forecasting researchers are eliciting and synthesizing beliefs and predictions from economists, AI-company staff, and superforecasters. Policy organizations are producing quantitative estimates (chip smuggling, regulation and model delays) that may directly inform policy arguments, legislation, and implementation. This work is getting attention, and some of it looks influential. The Yale Budget Lab built fiscal scenarios and a tax microsimulation on one set of AI growth forecasts; two prominent monetary economists used its scenarios in framing commentary on Fed policy. A study of model release delays in the EU and UK was reported as showing that data-protection rules, not the AI Act, slowed releases, as the EU considers amending each of these. Epoch AI’s estimate of chip smuggling to China is cited in CSET’s policy analysis relevant to chip-tracking legislation now moving through the US Congress. A widely covered intelligence-explosion paper leans on an economics model in claiming that AI R&D is approaching the threshold for self-sustaining acceleration. Little of this (as far as we know) has had careful, public, expert scrutiny. Critiques happen in elite academic seminars, but these are usually ~private and not entirely focused on practical impact. There’s some engagement in forums, podcasts, and social media threads, but in general these are not meaningfully vetted, thorough, nor with clear legible expertise. There are some strong ‘independent blog responses’ (e.g.) to some of this work, but even when these are systematic and seem rigorous, they are self-selected. The author chose to write about it and take a position: this makes it less reliable as a ‘neutral expert assessment’. On the other extreme, academic journal review in economics arguably has the neutrality and legible expertise, but it can take years, and at the end it reveals only which journal (if any) accepted it. Even after a usual ~‘six month turnaround at a single journal’ much of this work is no longer relevant.[3] A paper on AI workforce capabilities, scaling trends, or regulation may be out-of-date in months. Also, people are entering unfamiliar territory in a hurry, making mistakes more likely. This makes rapid peer review from experts in areas like growth, trade, labor economics, industrial economics, and psychometrics particularly valuable; along with plausibility checks from people on the technical side (computer science, ML researchers, engineers, and industry practitioners). We hope to provide more rigor without accepting the (far longer) timelines for traditional review. How our process fits the problem We’re neither an academic journal nor a public forum thread. A few things about our process seem to fit this problem: Speed. We commission public evaluations, yielding a public credibility signal in a single shot (not a long submit-reject-resubmit loop). We have a rich evaluator pool,[4] manage the process, and provide compensation, incentives for promptness and strong work, and evaluator prizes. Evaluators write a referee-style report with quan