# Investing — David Awad

**URL:** https://davidaw.ad/investing
**LLM version:** https://davidaw.ad/investing/llms.txt

Investing across eight asset classes — philosophy and approach per box.

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## [1/8] Venture Capital — VC — illiquid
The asset class I have publicly written about most. Deep-tech founders find my inbox because of a background in software, self-driving, and operating systems.
Market over product over team. I look for hair-on-fire problems and founders with courage plus brilliance — in that order. I deploy through angel checks and through funds, and I maintain the largest list of Arabic programming resources online.
Publicly disclosed: Groq (https://groq.com), Anduril (https://anduril.com), SpaceX (https://spacex.com), x.ai (https://x.ai/), Voyager Space (https://voyagerspace.com), camb.ai (fund) (https://www.camb.ai/), lune (fund) (https://www.lunedata.io/), moneyhash (fund) (https://moneyhash.io/), mamopay (fund) (https://www.mamopay.com/)

## [2/8] Real Estate — RE — turnkey, leveraged
Long-term wealth creation through leveraged hard assets in growth markets — Texas. Inflation runs hot, so fixed-rate debt on appreciating hard assets is the position.
Strictly turnkey. No rehab, no BRRRR, no value-add. The goal is boring, predictable cash flow with minimal operational complexity. STR exists as a sub-strategy, only where regulators are unambiguous.

## [3/8] Equities — Public — passive bias
I hold equities and remain skeptical of stock-picking edge for retail. The bulk is passive; active research is reserved for situations the index can't price.
When I do work a name, I work it like a credit analyst — DCF, unit economics, debt service coverage. I read quarterly letters from operators I respect; I do not read them all.

## [4/8] Options — Public — vol seller
Systematic vol-selling. Premium sold around catalysts — earnings, FOMC — when implied vol is rich relative to the realized distribution.
Iron condors and short strangles are the workhorses. I treat options as a separate discipline from stock-picking: different math, different risk, different psychology. The book is sized so a single bad print does not end the program.

## [5/8] Micro-SaaS — PE — sub-$1M ARR
Cash-flowing, unsexy software businesses bought at 2-4x ARR from operators who are exhausted, not distressed. Niches with high switching costs and low CAC from word-of-mouth.
The edge is patience and a willingness to buy businesses too small for institutional PE. Operate lean. Compound at 30-50% cash-on-cash by paying fair-but-unexciting prices.
Operating vehicle: axethrow.software (https://axethrow.software)

## [6/8] Private Equity — PE — leveraged growth
PE math: DSCR, debt service coverage, cash flow under leverage scenarios. The job is to stress the capital stack and walk away when it doesn't survive an unfriendly tape.
Through Luminance Capital Partners I perform technical due diligence on PE-backed companies — code audits, fractional CTO work, exit readiness — for sponsors in New York and Dubai.
Diligence shop: Luminance Capital Partners (https://luminance.capital)

## [7/8] Bonds — Fixed income — dry powder
Treasuries function as a cash-equivalent reserve — dry powder for opportunistic deployment into the other seven boxes when something repriced overnight.
No yield-chasing in credit. The job of this bucket is to be available, not to be exciting. A bond book that gets exciting is a bond book that failed.

## [8/8] Prediction Markets — Research — Kalshi / Polymarket
Active research interest. The question is whether event-implied probabilities diverge from pricing in adjacent options markets — and whether the divergence survives transaction cost.
Treated as a research area, not core capital allocation. Most of the work is the spreadsheet, not the position. The position is what's left after the spreadsheet has eaten everything that didn't pencil.