World Solve

Blockchain can record ownership transactions, but it doesn't inherently verify that the underlying digital artwork itself is original or hasn't been duplicated elsewhere. Solving this deeper authenticity verification remains an unresolved technical gap.

Consumer drones have become cheap and widely available, but affordable detection systems for identifying unauthorized or suspicious drone activity in sensitive areas remain limited. Closing this security gap remains an unresolved technical and regulatory challenge.

Companies sometimes stop providing security patches for older but still commonly used software or devices, leaving users vulnerable without clear warning. Establishing consistent, enforced support timelines remains an unresolved industry practice.

Building one app that works well across many different operating systems and device types often results in compromises that make it less polished than a dedicated, platform-specific version. Fully closing this quality gap remains an unresolved software engineering tradeoff.

Scammers can now use AI to generate convincing images of products that don't exist or don't match what's actually shipped, making it harder for shoppers to identify fraudulent listings. Detection tools on marketplaces remain inconsistently effective.

Attackers can sometimes convince phone carriers to transfer a victim's number to a new device, bypassing two-factor authentication tied to that number. Carriers have improved verification, but this attack method remains an ongoing security gap.

Fraudsters can use AI to generate entirely fake but convincing identities, complete with fabricated financial histories, to open accounts and commit fraud at scale. Detecting these synthetic identities before damage occurs remains a growing, unresolved challenge for financial institutions.

Scammers increasingly use AI to generate convincing fake job postings and interview processes designed to steal personal information or money from desperate job seekers. Platforms are still catching up to detect and remove these scams effectively.

When companies discontinue software or online services, users often lose access to their data or functionality with little warning or support for migrating elsewhere. Standardized, consumer-friendly transition processes remain largely absent from the industry.

Scammers increasingly use AI-cloned voices of family members to fake emergencies and pressure victims into sending money quickly, exploiting trust before people have time to verify. Reliable, fast verification methods to counter this remain underdeveloped.

Insurance companies increasingly use AI to assess claims, but there's limited independent, scalable auditing to ensure these systems aren't unfairly denying or delaying valid claims. Building trustworthy oversight for this remains an unresolved regulatory and technical challenge.

Even after an AI model is retired, techniques exist that can sometimes extract fragments of its original training data, posing ongoing privacy risks. Fully preventing this kind of data leakage after decommissioning remains unresolved.

AI hiring systems trained on historical hiring data can inadvertently learn and repeat past patterns of discrimination, even when explicitly designed to avoid it. Fully eliminating this bias while maintaining useful screening capability remains unresolved.

As AI-generated faces and voices become more realistic, some biometric security systems can potentially be fooled by convincing synthetic versions of a real person's face or voice. Strengthening these systems against this emerging threat remains an active challenge.

While hardware energy use gets some attention, the carbon footprint of running specific pieces of software, from development to daily use, remains largely unmeasured and unaccounted for. Building this tracking into standard practice remains unresolved.

Companies increasingly build products using AI models developed by others, but there's no consistent, scalable way to verify those models are safe and reliable before integration. This creates hidden risk throughout the growing AI supply chain.

Recommendation algorithms optimize for engagement, which can unintentionally narrow the range of content users are exposed to over time, reinforcing existing beliefs rather than broadening perspective. Designing systems that balance engagement with diversity of exposure remains unresolved.

AI-generated contracts, court filings, or legal notices can appear authentic enough to deceive people unfamiliar with proper legal formatting, creating new fraud risks. Detecting these fakes reliably remains an emerging unresolved challenge.

Without standardized measurement, it's difficult to compare the true environmental cost of different AI systems or hold companies accountable for improving efficiency. A consistent, transparent measurement standard remains undeveloped.

Automated trading systems can execute manipulative strategies at speeds far beyond human traders, and regulators still struggle to detect this kind of abuse in real time. Closing this detection gap remains an urgent unresolved challenge for financial oversight.