We don't ask you to take the pitch on faith. Here is every number behind it — the market data, the legal research, the technical landscape — with sources named and the honest caveats stated plainly. Including the data that argues against our own instincts.
Taken all together, Big Tech is a sliver. In the US, small business alone outweighs the Magnificent Seven by roughly 6.6× in revenue — and worldwide it isn't close. Meanwhile an estimated ~80% of what every business does is the same work. The leverage was always there; it was only ever waiting on coordination.
Caveat, stated plainly: the market-size figures above are illustrative order-of-magnitude comparisons drawn from public revenue aggregates, not a single audited dataset. The "~80% shared work" figure is a directional estimate of how much business activity is common back-office, operations, support, and sales motion versus genuinely differentiated. Treat these as the shape of the argument, not decimal-precise accounting.
| Comparison | Figure | Note |
|---|---|---|
| US small business vs Big Tech (revenue) | 6.6× | $13.3T vs $2.04T — the most conservative cut |
| US small + mid-market vs Big Tech | 11.6× | Bundle in mid-market |
| Worldwide small + mid vs Big Tech | ~30× | Even more lopsided globally |
| Big Tech share of US business revenue | 8% | Mag 7 vs small + mid |
| Big Tech share of world business revenue | 3% | Same comparison, global |
| US firms vs Big Tech firms | 36.2M vs 7 | Roughly 5 million to 1 |
| Worldwide SMEs vs Big Tech firms | ~400M vs 7 | Roughly 57 million to 1 |
| Business work that's shared across industries | ~80% | Customer, ops, marketing |
Roughly 80% of what every business does is the same work — it's why CRMs are everywhere. Their whole model is to solve that shared 80% once and bill each of us separately, forever, as if our problem were unique. It isn't. A problem millions of us share is one millions of us can solve together — one time. The imbalance was never about size. It was about coordination.
The "own your AI" hook is validated and commercially strong. Comparable campaigns confirm strong paying demand for the own-it-no-subscription pitch. And wins are driven by pre-built warm demand, not cold discovery — the strongest campaigns harvested demand built beforehand. The practical implication is a pre-warmed list converting early, which is exactly the warm-network, charter-member motion this plan uses.
Leading with "AI" underperforms. Research found AI-adoption framing associated with lower pledges and fewer backers, attributed to trust friction. So the durable pitch is ownership, continuity, and privacy — with AI as the engine, not the headline. That's why this whole site sells sovereignty first and says "AI is the engine, not the headline" everywhere.
And the finding that killed our original idea: cooperative crowdfunding has the weakest track record of any category. The original concept's differentiator — its mutualist/community structure — was also its weakest fundraising asset, and was structured to remove the founder from durable income. That finding, more than any other, drove the pivot to the current product-first, founder-anchored, two-entity design. We're showing you the data that argued against where we started.
Caveat: legal authorities are cited by name; the procedural ones (e.g. the annual Rev. Proc. series — currently the 2026 series) are reissued yearly and must be verified with counsel before any ruling request. This is research that informs the design, not legal advice.
The open-weight field is competitive with proprietary models for coding, reasoning, summarization, and structured workflows. The consistent case for local hosting is exactly the pitch the venture sells: privacy and data control, predictable one-time cost versus recurring subscription, no API rate limits or mid-project policy changes, and full customization.
| Model family | Best for | License |
|---|---|---|
| Qwen 3.5 / 3.6 | Best all-round local / agentic | Apache 2.0 |
| Gemma 4 | Efficient / edge / multimodal | Apache 2.0 |
| Phi-4 family | Constrained hardware | MIT |
| DeepSeek V4 | High-end coding / reasoning | MIT |
| Mistral families | EU-sensitive / low-latency single-GPU | Varies — verify |
| Llama 4 | General (with conditions) | Community license |
Standard self-hosted tooling: Ollama (single-node), vLLM (multi-node), Open WebUI (chat), Node-RED (workflow), Podman/Docker (packaging), with quantization (e.g. Q4_K_M) the standard technique for fitting larger models into available VRAM. Full licensing detail lives on The Stack page. These figures are point-in-time and will move — the architecture won't.
Colorado ULCAA, C.R.S. Title 7, Article 58 (incl. §§7-58-301, -304, -511 to -514, -1009). United Housing Foundation v. Forman, 421 U.S. 837 (1975); SEC v. W.J. Howey Co., 328 U.S. 293 (1946). Securities Act exemptions: Regulation D, Regulation Crowdfunding, Regulation S. Internal Revenue Code Subchapter T (§§1381–1388); §482; §7701(o). Capper-Volstead Act (8% dividend tradition). Rev. Proc. 2026-1 and 2026-3, superseding the 2025 series; Rev. Proc. 2015-41 (APA program); General Legal Advice Memorandum 2025-001 (periodic adjustments to high-value intangibles).
Hugging Face and community surveys of open-source / open-weight LLMs to run locally in 2026; comparative guides (Overchat, Codersera, Pinggy, ComputingForGeeks, AceCloud); self-hosted-AI guides (KDnuggets, DreamHost) for tooling and hardware context.
SBA Office of Advocacy (US small business count — 36.2M firms — and small-business share of GDP). National Center for the Middle Market (US mid-market ≈ $10T). World Bank / World Economic Forum (global SMEs ≈ 50% of GDP, ~400M firms worldwide). Magnificent Seven 2024 10-K filings (Big Tech aggregate revenue ≈ $2.04T). RSM Global / Oxford Economics (global mid-market sizing — the squishiest of the inputs). McKinsey / Salesforce / BLS work-allocation studies (the illustrative ~80/20 split between shared and genuinely differentiated business work).
UGREEN AI NAS and NASync campaign data (Kickstarter, tracked via BackerTracker / Kicktraq); The Conversation (Startnext path-dependence study); Song et al., Accounting & Finance (AI-framing underperformance); Shareable (co-op crowdfunding failure pattern); New Internationalist community-share figures; ScienceDirect (RegCF survival study); Qubit Capital (platform success rates); Public AI Switzerland and the Platform Cooperativism Consortium / HBR / SSIR coverage (cooperative-AI movement context).
Caveats on the market comparison. The 6.6× figure is the most conservative cut (US small business alone vs Big Tech); it grows to 11.6× with mid-market bundled in and to roughly ~30× globally. The Big Tech and US numbers are hard — drawn from 10-K filings and SBA data — but the global SME/mid-market split is estimated, anchored to the World Bank's "SMEs ≈ 50% of global GDP" and scaled to the US ratio. Note too that revenue ≠ GDP ≠ market cap: Big Tech's ~$22T market cap flatters it far more than its revenue does, so a revenue comparison is the unflattering-to-us, honest one. The 80/20 shared-vs-differentiated split is illustrative — the broad ratio is robust across studies, but the inner 35/28/17 breakdown of the shared portion is not three-decimal precise. And "small business" definitions overlap across sources, so the bands are deliberately drawn as order-of-magnitude, not to the dollar.
A standing caveat on all of it. Crowdfunding, model, and tax-procedure figures are point-in-time as of the dates noted and will change. Legal authorities should be verified in their current form with counsel — the procedural ones in particular are reissued annually. Nothing here is legal, tax, or investment advice. We'd rather show you the seams than imply a precision we don't have.
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