Reverse Engineer Available Funding

DRAFT — seed for deck design. A strategies-collection narrative; an operating doctrine for the grants/partnerships team. Audience: Reach/NCAD leadership and the development team. Grant-seeking framing throughout. Figures are from wealth/philanthropy reports and flagged ⚠ VERIFY — confirm against primary sources before presenting. Citations use stable hex codes per the LFM convention; definitions in ## Citations below and in the per-client registry (../../citations/registry.csv). Note: this narrative has no corpus strategy slug yet (nearest corpus home: topics/grant-prospecting-tools) — decide whether it becomes a corpus strategy or stays operating doctrine.

The through-line

Most fundraising effort is distributed roughly evenly across many prospects. Available funding is not — it follows a power law. A small number of foundations give out vastly more than the rest, ^{1 2} and a growing population of ultra-high-net-worth individuals — especially the newly, self-made wealthy — sit on enormous discretionary giving capacity. ³ The implication is simple and uncomfortable: stop spreading effort evenly; concentrate it where the dollars actually are.

The method is to reverse engineer the few funders who can write the biggest, most-aligned checks: study what they already fund, talk to them to learn the strategy behind it, recognize the pattern, and shape Reach’s ask to land in their sweet spot at the largest plausible size — instead of defaulting to a small, generic request.

The shape of the money

(All figures verified against primary sources 2026-07-28; the June draft’s Mellon/RWJ figures were ~2015-era aggregator data and are replaced below.)

• The pool is at a record. U.S. charitable giving hit $617.2B in 2025 — crossing $600B for the first time (+5.7% current dollars, +3.0% after inflation). ⁴
• Foundations are radically concentrated. Of 120,000 U.S. foundations, grantmakers giving $50M+ are just 0.1% of funders yet supply 40% of all grant dollars; ¹ the largest 1,000 (1%) account for roughly half to 60% of tracked foundation giving. ² Gates — the largest U.S. private foundation — paid out $8.0B in 2024; ⁵ Mellon ($540M, 2024) ⁶ and RWJF ($550M/yr) ⁷ each move over half a billion annually. The distribution is a power law, not a bell curve.
• A wave of new wealth is arriving. Cerulli now projects $124T transferring through 2048, of which ~$18T flows to charity. ⁸
• The ultra-wealthy population is large and growing. 556,850 UHNW individuals (net worth >$30M) globally in 2025, holding $63.8T; the U.S. is home to ~37% (~206,000); ³ global HNWI wealth reached a record $98.3T (Capgemini — investable-assets basis, do not mix with Altrata headcounts). ⁹

Why “new money” specifically

⚠ Framing (to validate): newly- and self-made-wealthy donors tend to have more discretionary capital (less locked into legacy structures), make decisions faster than large bureaucratic foundations, and are often seeking causes that express their identity and values. They are more reachable, more flexible on deal shape, and more willing to make a large bet quickly than a 40-person foundation with a fixed RFP cycle. The trade-off: less predictable, relationship- driven, and you have to find them before they’ve formalized their giving.

The method — reverse engineering a funder

1. Study the public footprint. IRS Form 990s reveal a foundation’s actual grantmaking history (who, how much, how often); ¹⁰ Candid/GuideStar profiles, websites, press, and the principals’ public statements fill in strategy and values. ¹¹ (For individuals: their wealth source, prior gifts, boards, public causes.)
2. Get the intro call. The 990 tells you what they funded; a conversation tells you why — the thesis behind the checks, what they’re trying to buy, what they’re tired of seeing. This is the highest-leverage step.
3. Pattern-recognize. Across their giving, find the recurring shape — the cause, the geography, the stage, the outcome they keep paying for.
4. Align to the sweet spot. Map Reach’s work onto that pattern and design the ask to the largest amount that still fits their pattern — not a default small request. Reverse the usual flow: start from what they can/ want to give, then shape the program to match.

Prioritize by the power law

Effort should follow dollars. A handful of deeply-researched, precisely-aligned asks to the biggest, best-fit funders will out-raise a hundred generic applications to the long tail. Rank prospects by capacity × alignment, and spend the team’s scarce time at the top of that list.

How to operationalize it

This is exactly what a funder corpus + agents are for. Reach already has a funder corpus under augment-it (augment-it/clients/reach-edu/ — dozens of funders, hundreds of documents); the [[agent-workflow-maxxing]] grants front describes the agent-assisted research, corpus, and brand-voice tooling. Reverse- engineering is the strategy; that corpus + those agents are the engine — they make studying many funders’ footprints and pattern-matching tractable for a small team. The 2026-07-28 mega-gifts-by-topic research (84 verified gifts across six topic lanes, per-strategy CSVs in the corpus) is the first worked product of exactly this method. (Cross-links: [[Agent-Workflow-Maxxing]], augment-it’s reach-edu corpus + the First-Pass Corpus Quality Scan plan.)

Proposed slide outline (Scroll-UI first, ~9 slots)

1. Cover — Reverse Engineer Available Funding.
2. Funding follows a power law — not a bell curve (the hero data slide).
3. Two concentrated pools — mega-foundations + new-money UHNWIs.
4. Why new money — more discretionary, faster, identity-driven (with the trade-offs).
5. The method — study footprint → intro call → pattern-match → align the ask.
6. Pattern recognition — find the recurring shape they keep paying for.
7. Size to the sweet spot — design the largest ask that fits their pattern.
8. Prioritize by capacity × alignment — effort follows dollars.
9. The engine — funder corpus + agents make it tractable (→ Agent Workflow Maxxing). Close.

Core messages (one-liners)

• “Funding is a power law — chase the few, not the many.”
• “The 990 tells you what they funded; the call tells you why.”
• “Start from what they can give, then shape the ask.”
• “New money: more discretionary, faster, looking for a cause to call its own.”
• “A few precise asks beat a hundred generic ones.”

Open questions / decide before deck build

• Verify every figure Done 2026-07-28 (primary sources fetched; stale Mellon/RWJ figures replaced; Giving USA/Cerulli/Altrata updated to current editions; $98.3T re-attributed to Capgemini). Hero numbers chosen: $617.2B (2025 record), ⁴ 0.1% of grantmakers = 40% of grant dollars, ¹ $124T transfer / ~$18T to charity. ⁸
• Find a clean, citable foundation-concentration statistic Found: Candid FY2022 — grantmakers giving $50M+ are 0.1% of funders and 40% of grant dollars; ¹ Foundation 1000 (~1% of funders) ≈ 50–60% of tracked giving. ²
• How explicit to be about the UHNWI / individual-donor play in a version that might be funder-facing (it’s candid operating doctrine).
• Tie-in depth to augment-it’s funder corpus — reference, or show it.
• Whether this narrative gets a corpus strategy slug of its own.

Citations