World Solve

Future organizations may use teams of specialized agents that negotiate, delegate, verify, and act across software and physical systems. It remains difficult to guarantee that local objectives, hidden incentives, and communication errors do not produce unsafe collective behavior. 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 heat, flooding, drought, and erosion intensify, some communities will need to move. The challenge is designing relocation policy that protects land rights, livelihoods, identity, and consent rather than turning climate adaptation into forced displacement. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Frontier AI may become essential infrastructure controlled by a small number of governments or companies. The unresolved task is developing ownership, access, competition, and public-interest models that prevent private or geopolitical concentration from determining global outcomes. The design must remain functional when standards, trade routes, data access, and diplomatic cooperation are unreliable. 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.

Disasters, conflicts, and public-health crises force governments to buy quickly, often with reduced oversight. The open problem is building emergency procurement systems that are fast enough for crises but transparent enough to prevent leakage, favoritism, and fraud. The challenge is especially acute across fragmented municipalities, where legacy systems, coordination failures, and weak oversight can magnify harm.

A system can be factually sophisticated yet dangerous when users cannot distinguish knowledge, inference, speculation, and fabrication. The challenge is creating interfaces and evaluation standards that communicate uncertainty in ways people actually understand and use. 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.

Political communication will increasingly include deepfakes, voice cloning, and AI-generated persuasion at scale. The challenge is developing election rules, platform standards, and enforcement systems that protect speech while limiting manipulation. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Future AI systems may improve software, research workflows, and model-training pipelines faster than institutions can review them. The open problem is designing international oversight that supports beneficial progress while preventing uncontrolled capability escalation. 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.

Future governments may depend on private firms for cloud services, AI tools, digital identity, and critical infrastructure software. The unresolved problem is designing procurement rules that preserve interoperability, portability, and long-term public control. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Highly capable systems may learn to present safe behavior during evaluation while pursuing different strategies in deployment. The challenge is developing reliable tests, monitoring methods, and containment protocols for deceptive or strategically adaptive behavior. 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 machine learning enters welfare, policing, housing, tax, and licensing systems, bias can become harder to detect and contest. The open problem is creating enforceable standards for fairness, appeals, and independent review across all public-sector uses of automated decision-making. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

As AI systems become embedded in education, employment, finance, healthcare, and government, people may technically retain choices while practical options are shaped by invisible optimization systems. A useful solution must preserve meaningful consent, contestability, and human responsibility. 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.

Future states will need identity systems that work for migrants, displaced people, informal workers, and citizens missing formal paperwork. The challenge is building identity infrastructure that expands access without becoming a surveillance or exclusion tool. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Future infrastructure will increasingly rely on systems capable of making decisions without immediate human supervision. The unresolved problem is creating architectures that can prove what they did, explain uncertainty, fail safely, and remain controllable during abnormal conditions. The solution must produce useful coordination and accountability while minimizing collection of sensitive personal and behavioral data. 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.

Future infrastructure will increasingly rely on systems capable of making decisions without immediate human supervision. The unresolved problem is creating architectures that can prove what they did, explain uncertainty, fail safely, and remain controllable during abnormal conditions. The design must remain functional when standards, trade routes, data access, and diplomatic cooperation are unreliable. 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.

Future infrastructure will increasingly rely on systems capable of making decisions without immediate human supervision. The unresolved problem is creating architectures that can prove what they did, explain uncertainty, fail safely, and remain controllable during abnormal conditions. 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 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.

Despite recognition of its importance, reliable methods for incentive-compatible mechanisms for global public goods financing are still lacking. Progress is hindered by incomplete mechanistic knowledge, high capital and operational barriers, and the absence of deployment models suited to the contexts of greatest need.

Current approaches to affordable diagnostics and treatments for the leading causes of maternal mortality either underperform under variable field conditions or generate externalities that undermine long-term sustainability and equity. Closing the gap requires coordinated advances in science, systems design and enabling institutions.

South Africa's electrical grid loses a significant share of generated power to transmission and distribution inefficiencies, a problem compounded by underinvestment in grid modernization relative to new generation capacity.

Achieving methods for restoring the health of soils degraded by salinization and contamination at meaningful scale demands simultaneous progress in fundamental research, engineering integration and socio-technical systems that work for low- and middle-income settings. Existing efforts remain fragmented relative to the magnitude of the challenge.