A complete system design for how humanity prevents, diagnoses, and heals — reconciling every iteration of this thesis into one definitive blueprint.
Five structural failures — not personality flaws, not underfunding — that guarantee bad outcomes
The American healthcare system does not fail because its individual participants are bad. It fails because its architecture makes good outcomes structurally impossible for the conditions that now represent 90% of health expenditure.
Conditions like Ehlers-Danlos Syndrome, MCAS, POTS, ME/CFS, and autoimmune overlap syndromes require synthesis across cardiology, rheumatology, immunology, neurology, and gastroenterology. No physician in the current system is incentivized, trained, or given adequate time to perform this synthesis. The average diagnostic delay for EDS is measured in years to decades. The information to make the diagnosis typically exists in the patient's records — scattered across providers who never see each other's notes in context. This is not a knowledge problem. It is a data architecture problem.
I know this because I lived it. I had two concierge physicians, medical teams at Mt. Sinai, Weill Cornell, and Yale — fifteen specialists across three of the most prestigious hospital systems in the country — and not one of them connected the dots. I diagnosed myself with narcolepsy and Ehlers-Danlos Syndrome, then spent months convincing those same specialists to run the tests that proved it.
Prevention generates no revenue in fee-for-service systems. The person who keeps you healthy is almost never the person who treats you when you are sick, which means critical continuity is lost. Seventy to eighty percent of chronic disease burden is driven by lifestyle and environmental factors, yet less than five percent of healthcare spending addresses these causes. The economics of the system actively punish keeping people well.
The current evidence hierarchy was designed for pharmaceutical regulation. It systematically excludes interventions that cannot be patented, cannot be blinded, or cannot be isolated from their delivery context — dietary interventions, movement practices, breathwork, plant medicines, psychedelic-assisted therapy. This is not scientific rigor. It is a category error that leaves the majority of the world's healing traditions outside the system, regardless of their efficacy.
Fee-for-service rewards volume. Specialists earn two to five times what generalists earn. Complex patients are financial liabilities — they consume more resources than their reimbursement covers. Insurance companies profit by denying care. Every incentive in the system points away from the outcomes patients actually need.
Insurance companies, PBMs, prior authorization departments, billing coders, hospital administrators — each extracting value without creating health. The system is optimized for claims processing, not care delivery. The patient is the least powerful actor in an architecture designed to serve every other participant's financial interest.
This chaos is exactly what incumbents point to when they argue against alternative healthcare. And they're not wrong — the chaos is real. But their solution — "come back to the pharmaceutical system" — is the equivalent of arguing that because the new world has pirates, we should return to the prison. The real answer is to build the coordination infrastructure without the extraction layer.
Protocols over institutions. Transparency over gatekeeping. A system design for healthcare that actually heals.
What follows is the definitive system architecture — refined across multiple iterations, reconciling the best structural thinking from every version of this thesis. The principle: transparent protocols with aligned incentives outperform opaque institutions with misaligned incentives.
Rigorous and epistemologically humble — the single most important intellectual contribution of this thesis
This is the answer to the question that will be asked first and loudest: "Where's the evidence for alternative medicine?" The answer is not to win the evidence argument retroactively. It is to build the system that generates the evidence prospectively.
M31's research has identified consciousness-based healing as a high-conviction emerging paradigm — scored at investable levels across the five signals framework. The CIA's Gateway Process documents, the convergence of contemplative traditions, and emerging research in psychoneuroimmunology all point toward the same conclusion: consciousness is not merely epiphenomenal to health. It may be foundational.
Within this evidence framework, consciousness-based modalities enter at Tier 2 where mechanistic research exists (meditation, breathwork, psychedelic-assisted therapy) and at Tier 3 where the evidence is primarily experiential and traditional (energy healing, frequency medicine, certain biofeedback practices). The critical point is that the system does not pre-judge these modalities. It generates the data that allows them to rise or fall based on observed outcomes. If something works, the data will show it. If it doesn't, the data will show that too. This is genuine scientific epistemology — not the constrained version that only studies what can be patented.
Not a support group. A load-bearing wall. A parallel intelligence system that outperforms institutions in specific domains.
After I was diagnosed, I found online patient communities — people with narcolepsy, EDS, and post-COVID conditions. What I discovered there changed the entire trajectory of this thesis: patients, in aggregate, know more about their conditions than the specialists treating them.
These communities were doing real-time pattern recognition. Triangulating symptoms across thousands of cases. Testing treatments through self-experimentation and sharing results. Building knowledge bases that no single physician could match. The narcolepsy community knew which medications worked best for which subtypes. The EDS community had mapped comorbidities that were barely mentioned in medical literature. The COVID long-haul community was identifying treatment protocols months before formal research caught up.
This is not anecdote replacing evidence. This is a different form of evidence — distributed, experiential, high-volume, and fast. It is the form of evidence that the institutional system dismisses and that the new system must integrate.
The Patient Graph (Layer 1) meets the Community Graph (Layer 5). AI systems can now ingest both — the individual's complete health record and the aggregate patterns from thousands of similar patients — to generate differential diagnoses and treatment recommendations that neither data source could produce alone. This is the synthesis engine that the siloed specialty system cannot build.
Four layers — because "how do you pay for this" is the first question any serious person asks
This is where most utopian healthcare designs fall apart. We are being specific and realistic.
Every person receives the core package: their systems physician relationship, AI-assisted patient graph, preventive monitoring, behavioral and lifestyle medicine, and access to Tier 1 interventions. Funded through taxation or mandatory contribution. Non-negotiable and universal. The economic argument is straightforward: prevention and early detection are dramatically cheaper than late-stage treatment. Every dollar spent on metabolic health stability in the 30s saves tens of thousands in diabetes, cardiovascular, and kidney treatment in the 60s. The capitated model makes these savings accrue to the entity paying for care.
Tier 2 and Tier 3 interventions covered through a combination of capitation top-ups and individual health savings accounts. Part required contribution (like Singapore's Medisave but expanded), part risk-pooled insurance. Patients have meaningful choice and skin in the game for interventions with less certain evidence, but the financial barrier is low enough that access is not determined by wealth.
Patients who contribute anonymized data to research and AI training receive direct compensation. Patients who participate in structured outcome tracking for Tier 2 and 3 interventions receive reduced costs. This creates a virtuous cycle: participation improves the evidence base, which improves the AI, which improves everyone's care, and participants are directly rewarded. The blockchain data sovereignty layer makes this possible and auditable.
For pharmaceutical companies and protocol developers, pricing is partially linked to real-world outcomes as measured by the system. If your drug works as well as claimed, you get full price. If outcomes fall short, the price adjusts automatically. This eliminates the current absurdity where drugs with marginal real-world benefit command premium prices based on carefully selected trial populations. The continuous data layer makes this measurable for the first time.
Information flows up, not just down. Clinical observation feeds research. The system learns.
In the current system, knowledge flows one direction: from research down to practice. Clinical observations — the patterns that frontline practitioners see every day — have no formal mechanism to flow upward into research. This is an extraordinary waste of signal.
The new system has a formal mechanism for bidirectional knowledge flow. Every therapeutic encounter generates structured data. AI continuously analyzes this data for emerging patterns — unexpected efficacy signals, adverse combinations, population-specific responses, novel comorbidity clusters. When a pattern reaches statistical significance, it is automatically flagged for formal investigation.
This means the system can identify that a particular herbal protocol is showing remarkable results for a specific subset of autoimmune patients — before anyone designs a formal trial. It can detect that a combination of breathwork and a particular peptide is reducing inflammation markers in post-COVID patients — before a researcher writes a grant proposal. The system becomes a massive, continuous observational study that generates hypotheses at speed and scale impossible in the current research architecture.
The community intelligence layer (Layer 5) feeds directly into this. Patient-reported patterns — the symptom triangulations, the treatment responses, the comorbidity mappings that communities identify — become structured data inputs for the knowledge system. What patients already do informally in Reddit threads and Facebook groups becomes formal, quantified, and actionable.
The core infrastructure already exists across three companies
The architecture described above is not theoretical. Its core components already exist.
The combination of Pearl's value-based care operating system, Garner's provider quality intelligence, and Oscar's tech-native insurance platform — augmented by AI-assisted diagnostics, blockchain-enabled data sovereignty, an expanded therapeutic spectrum, and the community intelligence network — constitutes the architecture for a fundamentally new healthcare system. One that prevents disease instead of treating it, diagnoses complex multi-system conditions in months instead of decades, and is epistemologically open to the full range of human healing traditions.
The opportunity is to assemble these pieces before the market recognizes their combinatorial value.
This architecture — the patient graph, the systems physician, prevention as default, the tiered evidence framework, community intelligence, on-chain standards, data sovereignty, and outcomes-based funding — is not a utopian fantasy. Its individual components are either already built, currently being built, or technically achievable with existing infrastructure.
What does not yet exist is the integration layer — the entity that connects the components, aligns the incentives, and maintains the design coherence as the system scales. That is the M31 thesis. That is the investment opportunity.
The old system treats organs, not people. It manages disease, not health. It generates revenue from suffering, not savings from prevention. It siloes knowledge and hoards data. Every structural feature that makes it profitable makes it terrible at its stated purpose.
The new system inverts every one of those features. And for the first time, the technology, the economic models, the regulatory momentum, and the cultural demand are all present simultaneously.
This is the architecture. Now we build it.