The Economics of Purpose

Diane Ortega ran the first set of numbers. She is 47, CFO of a regional foundation in Minneapolis that funds community health and civic capacity projects across the Upper Midwest. When BGO approached the foundation about funding two pilot deployments, she pulled out the calculation she runs for every capacity investment proposal: what would this intervention cost the receiving institutions if they obtained equivalent expertise through traditional channels?

Jonathan Reeves ran the second set. He is 52, a health economist at a university research center in Chicago. He does not study organizational capacity. He studies retirement and health outcomes, specifically the healthcare costs associated with purposeless retirement in older adults. When he saw the BGO deployment model, he ran a different calculation: what does purposeless retirement cost the healthcare system relative to what a BGO deployment costs to fund?

The two calculations come from different directions. They arrive at the same place: the case for funding the BGO model is not philanthropic. It is economic.

Diane’s calculation is the more straightforward one.

A twelve-week BGO deployment at two days per month, with Sage stipend, Native stipend, and AI infrastructure costs included, runs approximately $8,000 to $15,000 for a community institution. The range depends on the Sage’s domain, the Native’s support requirements, and the AI infrastructure tier the deployment uses.

The comparison: a twelve-week consulting engagement from a mid-tier management consulting firm for financial restructuring of the scope Raymond Okafor provided to the West Virginia health center runs $85,000 to $120,000 in the current market. An interim management placement for COO-level capacity runs higher: $150,000 to $200,000 for a twelve-week placement from a specialized interim firm, before the placement firm’s markup. A staff hire at a salary reflecting Carolyn Marsh’s experience and capabilities runs $250,000 to $350,000 annually, and requires benefits, management overhead, and the full-time commitment the FQHC cannot use.

The cost differential between a BGO deployment and traditional expertise channels is not marginal. For community institutions with limited budgets, the differential is often the difference between accessing COO-level thinking and not accessing it at all.

Diane funded two BGO pilots. Her reasoning was institutional effectiveness logic, not purpose philanthropy. The cost per institution served was the lowest she had seen for a strategic capacity intervention with measurable outputs. The institutional need was documentable. The deliverable was specified in advance. The foundation funded it because the numbers worked for the institutions receiving the deployments.

She is aware that the $8,000 to $15,000 cost includes a cross-subsidy from commercial BGO deployments. She factored this in. The foundation is not subsidizing the full cost of what it is funding. It is contributing to an ecosystem that shares costs across commercial and purpose deployments. This distinction matters to her, because it means the model has a path to sustainability that does not depend entirely on philanthropic capital.

Jonathan’s calculation requires more exposition, because it runs in the opposite direction of how most people think about purpose-deployment funding.

The starting point is the research on purposeless retirement and healthcare costs. Depression rates among recently retired adults are significantly elevated in the first two years following retirement, particularly among individuals who derived substantial identity and social connection from their work. Social isolation rates in retired adults without structured engagement rise sharply after retirement and remain elevated without intervention. Preventable hospitalization rates in socially isolated older adults are substantially higher than in engaged peers: estimates from the health economics literature put the excess at 30 to 50 percent for certain diagnostic categories.

Each of these outcomes produces healthcare costs. Depression in older adults drives primary care utilization, psychiatric care, and medication costs. Social isolation drives emergency department utilization, delayed diagnosis, and the chronic disease progression that results from reduced preventive care engagement. Preventable hospitalization is the most direct cost: hospitalizations that would not have occurred in an engaged, connected older adult average $15,000 to $25,000 per episode.

Jonathan estimated the healthcare cost differential between a deployed Sage and a matched non-deployed peer over a two-year period following retirement. His analysis is conservative: it includes only the isolation-driven costs and excludes the cognitive health dimension entirely, which the research predicts is also substantial but which has not yet been confirmed with BGO deployment data. The conservative estimate of the healthcare cost difference is several multiples of the cost of a BGO deployment.

The implication is specific: funding a BGO deployment is cheaper than paying for the healthcare consequences of not funding it, if the research predictions hold at the individual level. Jonathan’s calculation is prospective and research-based. It is not yet confirmed by longitudinal data from deployed Sages. The data is being collected. The calculation is based on what the research literature predicts.

The insurance coverage argument runs through Jonathan’s calculation and the BGO deployment data simultaneously.

Physical therapy is covered by insurance because it produces measurable functional outcomes that reduce downstream healthcare costs. The coverage logic is: the intervention costs less than the downstream costs it prevents, and the evidence base is sufficient to quantify the prevention effect. Insurance companies cover physical therapy because the economic case is made.

A BGO deployment, if the cognitive and health outcome data from deployed Sages shows what the research predicts, meets exactly the same coverage logic: an intervention that costs $8,000 to $15,000, which prevents healthcare costs in the range of $50,000 to $100,000 over two years in the same individual, is a coverage case that an insurance actuary can evaluate on the same terms they evaluate any preventive intervention.

The argument is prospective. The BGO outcome data does not yet exist at scale. The cognitive tracking infrastructure from Series 1 and Series 4, the social monitoring from Series 8, and the purpose engagement tracking from the deployment AI are generating the data that would make the argument. The data will take two to three years of deployment cohort follow-up to reach the scale required for an insurance coverage discussion. The argument is honest about this.

Jonathan showed his analysis to a foundation program officer who was evaluating a BGO grant application. She read the healthcare cost differential calculation and said: “This is the first thing that made the model fundable to me. Not the purpose story. The numbers.”

The purpose story is what makes BGO meaningful. The numbers are what makes it sustainable. Both are necessary. Neither alone is sufficient.

The cross-subsidy model is the third economic leg.

Commercial BGO deployments place Sages with professional services firms, corporations, and mid-sized organizations seeking specific expertise at project rates. A senior finance executive with thirty years of capital markets experience, deployed to a private equity firm for a twelve-week due diligence project, generates revenue at a different rate than a deployment to a rural health center. The commercial rate reflects the market value of the expertise in a commercial context. The purpose deployment rate reflects the cost structure the community institution can sustain.

The model cross-subsidizes: commercial revenue funds purpose deployments through a ratio that BGO is testing across the pilot cohort. The ratio is not yet known at scale. The commercial market for high-quality, short-duration, senior-level expertise on specific projects is real. The question is whether the commercial volume is sufficient to cross-subsidize purpose deployments at the ratio required.

This is not a tested sustainability model. It is a proposed one. The pilots are generating the data that will determine whether the ratio works. The piece would not be honest if it described the cross-subsidy model as proven.

Three failure modes for the economic argument, named specifically because a publication that only describes the upside is not useful.

The cognitive health data does not show what the research predicts. If the longitudinal data from deployed Sages does not show meaningfully better cognitive and health outcomes than matched non-deployed peers, the insurance coverage argument loses its foundation. The purpose story remains meaningful. The numbers do not support the coverage case. This is the scenario that BGO’s commitment to publishing all outcome data, including null results, is designed to address. If the data does not confirm the hypothesis, BML publishes that.

The commercial market does not generate sufficient volume to cross-subsidize purpose deployments at the required ratio. The commercial BGO market is real but not yet tested at the scale the cross-subsidy model requires. If the commercial volume is lower than projected, or if the Sages whose expertise commands commercial rates are not the same Sages whose expertise is most needed in community institutions, the cross-subsidy model does not work as designed.

Foundation funding is time-limited. Most foundation grants run two to four years. If the BGO model does not reach commercial self-sufficiency or insurance coverage before the grant period ends, the purpose deployment program faces a gap. This is the standard sustainability risk for any philanthropically launched model. It is not unique to BGO. It is not resolved by the economic argument alone.

Diane’s foundation funded two deployments. Jonathan’s analysis contributed to the case. The model is in early operation.

The economic argument is not a proof of sustainability. It is the strongest framing available for the funding case at this stage of the model’s development. The institutional cost differential is real and documentable now. The healthcare cost differential is research-based and prospective. The cross-subsidy model is proposed and under test. The insurance coverage argument is the destination the data is being built to reach.

The foundation program officer’s sentence is the honest summary of where the argument stands: the purpose story is the meaning. The numbers are the sustainability. The model needs both, and both are in development.

What Exists Now, What Is Coming, and What Requires Time

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Foundation funding for BGO pilot deployments is available now through aging-focused and community health foundations. The institutional cost differential argument is documentable from current deployment data. No insurance coverage for purpose deployments as a health intervention exists. No federal program integrates the BGO model. The economic case made here is based on available cost data and research literature.

Within one to two years, the first foundation grant specifically for BGO deployment scale; academic partnership for independent evaluation of deployment outcomes; initial insurance company conversations using preliminary cognitive outcome data from the first BGO cohort.

Within three to five years, insurance coverage for BGO deployments as a preventive health intervention, pending the prospective longitudinal outcome data the BGO ecosystem is now generating; federal program integration through Administration for Community Living or AmeriCorps Seniors; commercial BGO deployments cross-subsidizing purpose deployments at a tested and documented ratio.

The purpose story is the meaning. The numbers are the sustainability. Both are being built.