World Solve

AI-designed biological molecules may be created faster than their safety, ecological, and clinical properties can be characterized. The open problem is building evaluation pipelines proportionate to capability and deployment risk. 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.

Large-scale fermentation may compete with food, land, and water if it depends on agricultural inputs. Future systems need safe, affordable, low-carbon feedstocks that do not shift environmental burdens to poorer regions. 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.

Predictive policing can reinforce historical bias even when built from apparently neutral data. The open problem is designing standards that make public safety tools testable, reviewable, and defeasible when they cause harm. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Biological design tools are becoming more accessible while oversight remains fragmented across countries and institutions. The challenge is detecting dangerous activity without suppressing legitimate research or creating an intrusive global surveillance system. 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.

Building codes often use historical temperature, rainfall, and wind data. Future standards must remain useful across uncertain climate trajectories without making housing unaffordable. 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 quantum networks will need coexistence with conventional fiber, satellites, and noisy metropolitan infrastructure. A major unresolved problem is developing practical repeaters, interfaces, standards, and operational security for real-world deployment. 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.

Public agencies will buy models, datasets, inference services, and automated workflows from vendors whose systems may not be easy to inspect. The unresolved problem is creating contract standards that make public AI procurement auditable, portable, and lawfully contestable. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Quantum capabilities could transform optimization, chemistry, intelligence, and cryptanalysis while becoming concentrated in a few states. The challenge is preventing destabilizing secrecy and unequal access without blocking peaceful scientific collaboration. 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.

AI systems trained on globally diverse data may encode incompatible assumptions about fairness, authority, family, privacy, and acceptable risk. The challenge is creating pluralistic evaluation methods that protect universal rights without imposing one culture's values as a universal standard. 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.

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.

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.

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.

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.

Materials may be valuable but poorly documented, contaminated, or unavailable at the needed time. Standardized audits, uncertainty-aware marketplaces, and logistics coordination could make reuse competitive with disposal. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with measure uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Digital contracts can potentially be modified after signing in ways that aren't always immediately detectable without specialized forensic review. Building this verification directly into standard contract systems remains incompletely adopted.

Some advertisements use AI-generated voices without always clearly disclosing this to listeners, and consistent labeling standards for synthetic voice content remain unevenly enforced. Closing this transparency gap remains an unresolved regulatory and design issue.

Verifying that voting machine software hasn't been secretly altered requires specialized technical audits that aren't consistently performed everywhere elections take place. Making this verification standard and accessible remains an unresolved election security challenge.

Attackers can still forge email headers to make messages appear to come from trusted banks, companies, or government agencies, despite improved authentication protocols. Full adoption and enforcement of stronger email verification standards remains inconsistent.

Some companies design cancellation processes to be far more complicated than sign-up, discouraging customers from leaving even when they want to. Consistent regulation or design standards preventing this practice remain unevenly enforced across countries.