World Solve

Future workers may combine employment, freelancing, caregiving, entrepreneurship, and AI-assisted production. Benefits must follow people across contracts without making innovation or small-business hiring impossible. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

gig workers without benefits or protections remains one of the most urgent challenges of our time. Systems currently in place are inadequate for the scale and complexity of the problem. A successful approach must work where resources are scarcest and demonstrate clear, measurable results. It should identify possible harms, respect local agency, and show how the solution can be maintained and expanded by the communities it is meant to serve.

AI, automation, caregiving, health events, and climate disruptions may make stable employment less common. The challenge is designing benefits, credentials, insurance, and taxation systems that follow people across changing forms of work. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Temporary or permanent height exchange for social or economic benefits creates new bodily hierarchies and health risks. The problem is protecting bodily integrity from becoming a tradable commodity linked to status.

Engineered microbes, insects, crops, and gene drives may offer major ecological benefits but could spread beyond intended boundaries. The unresolved problem is developing reversible, monitorable, and internationally governed release frameworks. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Many adaptation measures protect health, social stability, and ecosystems rather than producing direct revenue. New financial structures are needed for projects whose greatest value is avoiding losses that may never be visibly recorded. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Governments may use health and behavioral data to predict outbreaks, allocate care, or target interventions. The open problem is enabling public-health benefits while preventing discrimination, mission creep, and privacy erosion. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Claims about quantum advantage often depend on narrow benchmarks or idealized assumptions. The challenge is creating reproducible measurements that identify when quantum systems deliver meaningful benefits over classical alternatives. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

As benefits move online, errors can become opaque and difficult to reverse. The challenge is creating fast, accessible grievance systems that help ordinary people correct eligibility mistakes without requiring legal expertise. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Governments will increasingly rely on automated systems to allocate benefits, flag risks, prioritize inspections, and recommend enforcement. The unresolved problem is creating governance structures that make these systems transparent, contestable, and accountable without disabling public service delivery. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Genomic and biomarker-driven therapies will transform oncology and rare disease care, but high costs and limited capacity risk concentrating benefits in wealthy populations. Fair pricing, global data-sharing, and low-cost diagnostics will be critical.

Workers lack contracts, benefits, and protections. Gradual formalization and portable benefits can strengthen security.

Platform workers often have no benefits or stability. Regulatory frameworks for gig work can extend protections.

Fragmented grids and regulatory regimes prevent efficient sharing of electricity between countries, leading to supply imbalances. Harmonized regulations and cross-border transmission projects could unlock benefits.

Ride-hailing and delivery platform workers often have no formal contracts, benefits, or dispute-resolution mechanisms. Portable benefits platforms tied to worker digital IDs could extend protections across platforms.

Economic growth has disproportionately benefited urban elites, leaving rural and lower-income populations behind. Progressive tax reform and targeted rural investment programs could help redistribute growth benefits.

Millions of delivery drivers and ride-hailing workers in South and Southeast Asia operate without employment benefits, workplace injury compensation, or collective bargaining rights, leaving them economically precarious.

Migrant workers moving across regional trade corridors lose accrued pension contributions and social coverage when returning home, eroding long-term financial security.

Promises made to public workers decades ago about retirement benefits sometimes weren't matched with enough funding set aside to actually cover them. This leaves future governments facing financial strain that current officials often avoid addressing.

When an algorithm denies someone benefits or flags them as a risk, it's often unclear who is responsible for reviewing or correcting that decision. This accountability gap grows as governments rely more heavily on automated systems.