AI Data Companies: Thoughts on Org / Comp Structure I've been thinking about the right org structure and compensation philosophy for AI data companies. Rough thoughts: (THIS IS NOT LEGAL ADVICE) 1. Avoid incorporating as a C-Corp due to double taxation of dividends. Instead, create a S-Corp or LLC. 2. Don't raise venture money, at least initially. Raise money from family offices or angels since VCs can't invest in a S-Corp or a LLC. If the company does turn out to have durable equity value, you can always switch to a C-Corp (S-Corp might be better for this reason than LLC, but pls consult your lawyer). 3. Raise the amount (+ buffer) you need to get to cash flow positive (I think this is likely sub-5M, probably even 1-2M). Obviously this is easier said than done for most, but if you have a strong background and a differentiated POV + lab connects, there are TONS of folks who'd be interested in this kind of structure. 4. (this will be controversial) Don't give any equity to employees. Everyone should be paid a relatively standard base + a highly variable bonus, which is a % of gross profit they are tied to, similar to a hedge fund. 5. After all employees are paid out, keep 30% of the remaining cash on the balance sheet and dividend out the rest to shareholders. Re org structure, run the company like a multi-manager hedge fund. (a) small P&L-owning pods per lab or vertical (b) a thin shared platform (c) an independent QA function with veto power, since QA is SUPER important (d) a tiny full-time core, and (e) an elastic expert network of contractors. In general, I think a hedge fund style org and comp structure is the best way to go here. Most hedge fund employees don't own equity in the underlying management company, but nobody ever complained of not owning equity in Jane Street. AI data companies are similar to hedge funds because: (a) The edge decays. A hedge fund’s alpha erodes as others find the same trade. A data company’s edge erodes too: today’s frontier data (a new domain, a new eval format, a new kind of expert annotation) becomes commoditized within a year or two, and labs bring capabilities in-house or models learn to generate it themselves. (b) Returns come from people, and you can attribute them. At a fund you know which PM or desk generated the P&L. At a data company you know which team owns which lab relationship, which project, which data vertical. When contribution is measurable, a cut of gross profit is a fairer and more motivating incentive than a slice of a cap table whose value nobody can predict. (c) It doesn’t need much capital. Like a fund’s management company, a data business is mostly people and some tooling. Once it’s cash-flow positive, more capital doesn’t make it grow much faster. Growth is limited by finding great people and earning lab trust, not by money. Raising a big round just dilutes owners to fund spending the business doesn’t need. tl;dr if the business looks like a high-margin, people-driven, cash-generating machine with uncertain long-term moat, you should structure it like one. Optimize for distributable cash and paying the people who generate it, and keep the option to become a “real” equity story if one emerges.