Investing

Investments

I back exceptional founders at the pre-seed and seed stage - primarily in AI infrastructure, developer tools, and B2B software. Read more about my investment thesis.

Portfolio

Companies

Where I've put conviction to work.

Framework

Investment Thesis

I evaluate deals through four lenses. The first two are largely objective and carry the most weight - they're the clearest signal in a noisy market. The last two require judgment and taste; I have views on them, but I hold them with appropriate humility.

Weighted Most Heavily · Objective
01 Objective

Founder Quality

Prior Experience · Domain Fit

Two questions I'm always asking: have they built from zero to one before, and does their background give them an unfair edge in this specific problem? The best founders have lived the pain they're solving - they don't need to discover the problem, they need to solve it.

Founder-Market Fit Examples

AWS ML engineers who spent years hand-optimizing GPU training jobs, now building an AI infrastructure platform to automate exactly that - the problem is their lived experience.

A former ER physician co-founding a clinical workflow automation tool with a strong full stack engineer - they've felt the friction daily, know the stakeholders, and can open doors through their network immediately.

A generalist founder opportunistically entering a regulated industry (healthcare, finance, legal) with no prior exposure to the customer - solving a problem they read about, not one they lived.

02 Objective

Traction

Revenue · Pilots · DAU · Velocity

Cold numbers - revenue, DAU, pilots signed. But equally important: how quickly did they get there? A team at $10K MRR in month two tells a completely different story than one at $10K MRR after 18 months. Speed signals real conviction in the market.

What I'm Looking For

Paying customers is the highest signal: for example, revenue in the first 90 days of company formation. Even $100/month can be meaningful signal at pre-seed.

Enterprise pilots where the customer is actually paying or committing real resources - not just a soft LOI or email commitment.

12 months post-launch with "lots of warm conversations" and no signed contracts. Activity is not traction.

Supporting Signal · Judgment Call
03 Judgment

Product

Pain Point · Scalability · PMF Signals

Is this solving a serious and urgent pain, or just a nice-to-have? And can it evolve to address bigger problems as the company scales? I'm listening for customer quotes that sound like "we can't imagine going back" rather than "this is useful sometimes."

What Matters to Me

Organic referrals - proof of existing customers recommending to others unprompted is the earliest PMF signal I know of.

Natural upsell paths - a product that can grow with the customer and tackle bigger adjacent problems over time is far more interesting than a static tool.

I don't obsess over moats at pre-seed/seed unless it's a deep tech or scientific breakthrough. Investors claiming to find moats in all early stage startups are full of ****.

04 Judgment

Market

Bottoms-Up TAM · Competitive Landscape

I'm skeptical of top-down market sizing. Saying "we're targeting the $200B logistics market" tells me nothing useful. I want bottoms-up: how many customers can realistically buy this, at what price, and why now?

What I Want to See

Bottoms-up: (# of realistic target customers) × (realistic ARPU) = a number you can actually defend. E.g., 50K US accounting firms × $2,400/yr = $120M TAM.

Honest competitor analysis - who actually competes for this customer's attention and budget, and what's the realistic displacement story?

A small but captive niche often beats a vague massive market at early stage - depth before breadth.