Modeling the World's
Capital Inefficiencies

Our foundational, long-term research is dedicated to Deconstructing Systemic Inefficiency, investigating the common, underlying causal components that create and sustain friction across all sectors, from broken market logistics to policy effects. We hypothesize that these major inefficiencies often share similar data DNA, meaning they can be sourced and solved with the same core, scalable solution pattern. To realize this, we're building the Inefficiency Model, a rigorous quantitative framework that precisely measures the systemic friction and the high marginal utility created by its removal. This research effort directly fuels Ryndel Labs' Phase 2: The Scalable Solution Model, establishing an algorithm that efficiently discovers and prioritizes the most globally inefficient systems. This enables us to deploy efficient solutions across sectors in a scalable manner and learn from every implementation.

Right now, there is no central system connecting funders with early projects or niche research that matches their mission. If a foundation wants to fund breakthroughs in Alzheimer’s, they only see surface-level biomedical projects, not the computer science models, neuroeconomic studies, or chemical processes that could indirectly advance it. Existing databases are siloed by field and language, creating huge informational gaps. The inefficiency is in the discovery layer, where interdisciplinary or under-the-radar work is invisible. Building a semantic engine that connects related but distant ideas would let funders support entire solution networks instead of isolated projects.

Everyone is optimizing AI’s performance. Nobody is modeling how much autonomy humans should delegate and how fast. Huge inefficiencies are coming from both overtrust and undertrust. Overtrust happens when black-box AI is used where it should not, leading to bad calls and accountability gaps. Undertrust happens when institutions bury AI decisions under layers of bureaucratic review, slowing everything down. Modeling this autonomy mismatch, figuring out where humans should retain judgment versus where machines can fully take over, can help funds, regulators, and AI-first organizations design smarter decision systems that actually match their goals.

Private equity and search funds rely heavily on standardized datasets that favor large, visible companies while ignoring strong operators in smaller markets or overlooked sectors. This creates inflated competition around the same pool of assets and leaves many profitable targets undiscovered. The inefficiency sits in sourcing and visibility, not in capital itself. A platform that connects academic research, local innovation data, and early financial signals could uncover undervalued targets long before they reach traditional deal flows, giving allocators a real edge and creating fairer access to growth opportunities.

The disabled market is one of the largest and most ignored inefficiencies in the global economy. Over 1.6 billion people hold an estimated 13 trillion dollars in disposable income, yet most products, marketing, and employment systems overlook accessibility until it becomes a compliance issue. Companies waste money on redesigns, miss innovation, and fail to reach loyal consumers and capable employees. Disabled communities have historically driven major inventions like voice-to-text, curb cuts, and remote controls. Marketing budgets are wasted on inaccessible campaigns, and hiring pipelines fail to reach talented workers who could solve critical problems. A platform that models this data and connects companies, investors, and advocacy groups could turn accessibility from an afterthought into a core design principle that drives both social impact and financial returns.

Data centers consume enormous amounts of water for cooling, yet reporting is inconsistent and often hidden. This creates blind spots for regulators, investors, and researchers trying to assess environmental trade-offs. The inefficiency lies in missing visibility rather than resource use alone. A system that tracks water usage, cooling technology performance, and local scarcity metrics could help redirect investment toward sustainable computing infrastructure and allow stakeholders to understand the true cost of AI growth on water resources.

Many small farmers operate under contracts that tie them to single buyers or input providers, limiting their freedom to change crops or pricing. These contracts often hide the true costs of production, create dependency loops, and block access to fair credit. The inefficiency comes from information asymmetry and poor transparency across agricultural value chains. A platform that aggregates soil data, yield performance, and market information could give both funders and farmers a clear view of fair value, making it easier to design financing systems that support independence rather than dependence.

Financial regulation is reactive and often lags behind market inefficiencies. Patterns in filings, enforcement actions, and trading data emerge long before regulators act, leaving capital misallocated or risks unaddressed. The inefficiency is in the slow synthesis of available information. A platform that continuously scans and links regulatory documents, market movements, and research insights could provide early signals to regulators, investors, and policy researchers, helping them act before inefficiencies turn into crises.

Capital flow between countries is still slowed down by fragmented data and uneven access to identity verification. Investors in developed markets rarely see credible opportunities in emerging ones, not because they do not exist, but because of mismatched documentation, inconsistent reporting, and limited visibility into local markets. This traps liquidity in familiar regions while economies that need capital remain underfunded. A platform that standardizes and contextualizes these projects across borders could lower due diligence costs, reduce perceived risk, and help capital move more freely and fairly around the world.