Most AI labour research stops at estimating which jobs are exposed; this project asks when AI-driven disruption in Southeast Asia’s export-service economies could cascade into a broader economic and governance shock—and what governments can do before it does. Mentees will build and stress-test early-warning indicators, transition scenarios and policy triggers for anticipatory action.
About the project
THE QUESTION
Under what observable conditions could increasingly capable AI systems turn concentrated disruption in Southeast Asia's export-oriented services into wider fiscal, social or governance instability—and which actions should governments prepare before those thresholds are crossed?
WHY THIS MATTERS
Recent ILO research finds substantial exposure to generative AI across ASEAN but limited widespread labour disruption so far. The key gap is therefore not another headline estimate of jobs at risk. Policymakers need to distinguish exposure from adoption, augmentation from displacement, and a manageable sectoral transition from a shock that threatens state capacity.
Southeast Asia offers an important test case because economies vary widely in their dependence on export services, labour-market structure and institutional preparedness. The Philippines' IT-BPM sector will be the anchor case because it combines significant export importance, concentrated geographic exposure and fast-changing client demand.
PROJECT DESIGN
The project will be completed by three mentees working as one team across three linked workstreams:
Evidence and indicators. Build a causal map connecting AI capabilities and adoption to task change, entry-level hiring, wages, firm behaviour, service exports, household resilience and government capacity. Create an indicator dictionary specifying data sources, update frequency, limitations and confidence.
Scenarios. Develop three plausible pathways through 2031: gradual augmentation, uneven sectoral disruption and rapid substitution. The team will formulate decision-relevant forecast questions and identify which observable developments would increase or reduce confidence in each scenario.
Policy triggers. Identify a small portfolio of government responses and stress-test them across the scenarios. The aim is to separate no-regret measures that should begin now from contingent actions that should be triggered only when specific warning signals appear.
The empirical scope will be the Philippines plus one contrasting ASEAN economy selected in the first two weeks using transparent criteria: economic structure, AI exposure, institutional preparedness and data availability. The framework will be designed for later regional extension, but the project will not rank all ASEAN governments or estimate a single number of jobs that AI will eliminate.
OUTPUTS
The team will produce a concise working paper, an open indicator dictionary and evidence appendix, three scenario briefs, and a policy-trigger matrix accompanied by a two-page policy brief. These outputs can inform AISA's proposed economic-scenarios programme for ASEAN economies through 2031.
Theory of change
Transformative AI is not only a model-safety challenge; it is also a transition-governance challenge. If rapid capability gains diffuse into globally traded services faster than institutions can adapt, concentrated shocks to employment and exports could weaken fiscal capacity, public trust and inter-agency coordination. Those conditions could make it harder for governments to evaluate high-risk systems, respond to AI incidents and cooperate on safeguards, while increasing pressure for rushed deployment or competitive deregulation.
This project addresses that pathway by turning broad concern into decision infrastructure. It will identify measurable warning signals, clarify how sectoral disruption might escalate, and connect those signals to pre-developed policy options. Earlier detection and rehearsed responses can help preserve the institutional capacity needed to govern increasingly capable AI systems.
The theory of change is: research identifies credible escalation pathways and indicators; regional experts stress-test them; AISA can then use the framework in policy dialogues and simulation exercises; governments and partners can adapt the strongest options into monitoring and preparedness routines.
The project does not assume that labour disruption alone becomes catastrophic. It studies a plausible amplifier of advanced-AI risk and develops practical measures for preventing a difficult transition from degrading the institutions on which AI safety governance depends.
Your role
Mentees will participate as junior co-researchers with meaningful ownership, not as general research assistants. After a common scoping phase, each mentee will lead one linked workstream:
Evidence lead: causal mapping, literature review, indicator selection and data-quality assessment.
Scenario lead: scenario construction, forecast questions and sensitivity analysis.
Policy lead: comparative policy research, trigger design and structured stress-testing.
All mentees will contribute to country selection, peer-review one another's work, participate in expert discussions and co-author the final paper and policy products. Mentees will have autonomy to propose methods and follow promising evidence within the agreed scope.
AISA will set the core research question, approve methodological and publication gates, run a weekly team meeting, hold regular individual check-ins, and provide substantive feedback on major drafts. The team will use a shared workplan, evidence log and weekly written updates.
Prerequisites
Applicants must:
Be able to conduct rigorous literature or policy research and write clearly in English.
Have basic quantitative literacy and be comfortable assessing the quality and limitations of public data.
Be able to commit at least 10 hours per week for the full programme.
Demonstrate serious interest in transformative-AI governance and economic transition.
Applicants should also bring strength in at least one of the following areas: labour or development economics; public policy or political economy; quantitative data analysis using spreadsheets, R or Python; structured forecasting or scenario methods; or Southeast Asian political and economic research.
Regional experience is valuable but not mandatory. Applicants do not need to be advanced programmers. More important are intellectual honesty, careful handling of uncertainty, reliability and the ability to distinguish evidence from inference.
Application question(s)
Mentees will participate as junior co-researchers with meaningful ownership, not as general research assistants. After a common scoping phase, each mentee will lead one linked workstream:
Evidence lead: causal mapping, literature review, indicator selection and data-quality assessment.
Scenario lead: scenario construction, forecast questions and sensitivity analysis.
Policy lead: comparative policy research, trigger design and structured stress-testing.
All mentees will contribute to country selection, peer-review one another's work, participate in expert discussions and co-author the final paper and policy products. Mentees will have autonomy to propose methods and follow promising evidence within the agreed scope.
AISA will set the core research question, approve methodological and publication gates, run a weekly team meeting, hold regular individual check-ins, and provide substantive feedback on major drafts. The team will use a shared workplan, evidence log and weekly written updates.
About the mentor
I am a co-founder of AI Safety Asia (AISA), where I am learning alongside researchers and practitioners about how increasingly capable AI may affect Asian economies and public institutions. My interests include economic transition, AI governance and the practical challenges governments face when responding to uncertainty. As a mentor, I hope to offer regional context, a clear research structure and thoughtful feedback while giving mentees room to shape the work and challenge my assumptions.
Before AISA, I worked at Google AI on inclusive-AI initiatives and language resources for underrepresented communities. I later served as Head of Data and AI Ecosystems at the World Economic Forum, where I helped establish the AI Governance Alliance and worked across technology, policy and multilateral cooperation. These experiences showed me both the potential of global collaboration and the importance of grounding AI governance in local economic, institutional and cultural realities.