What are Llama Ventures's red flags?

No crypto. No technical cofounder.

What sectors does Llama Ventures invest in?

Llama Ventures focuses on AI, Consumer, Edtech, Enterprise, Fintech, Hardware, Healthtech.

What is the typical check size for Llama Ventures?

Llama Ventures typically writes checks between $500000M and $5000000M.

Claimed & calibrated

How to Pitch Llama Ventures

Q: How do I pitch Llama Ventures?

To pitch Llama Ventures, practice against our AI simulation built from their investment thesis: I invest in one sentence. Every deal has to compress into "what I'm investing in is ___" — one sharp, specific, defensible standout, in either the people or the business. If I finish a deck and that sentence doesn't form in my head, that's my answer. On people, the standout has to be one of a few real profiles rather than a resemblance to one: a researcher who has spent a decade on the exact problem the company now sells; a genuinely complementary technical × commercial pairing; a vertical insider by family or career, not someone who sold into the industry for five years; a network that actually solves cold start; or a founder who has already exited or built a real audience. On the business: a community with real numbers, paying retention at a humble valuation, a step-change in technology rather than an increment, or a moat I can quantify in the time and money a competitor would need to reproduce it. I spend my time in AI infrastructure, robotics data and control, chip design, and physics-grounded industrial AI — because in those I've built the industry map myself, which is what lets me tell a real business from a capability everyone will soon get for free. You have 30 minutes to answer real questions like "Before we talk about the product — how did you two meet? How many times have you worked together before this, and what was the setting the relationship grew out of?" and "Take the word 'AI' out of that sentence and say it again. Whose model is it, which part of the data is actually yours, and what breaks if your vendor changes their API next quarter?". Get a 12-dimension scored verdict.

A practical guide based on our AI simulation of Llama Ventures's investment thesis — distilled from public memos, interviews, and track record. Practice your pitch against the AI persona and get a 12-dimension scored verdict.

Calibrated by Llama Ventures. The thesis, red flags, and questions are theirs.

Investment Thesis

I invest in one sentence. Every deal has to compress into "what I'm investing in is ___" — one sharp, specific, defensible standout, in either the people or the business. If I finish a deck and that sentence doesn't form in my head, that's my answer. On people, the standout has to be one of a few real profiles rather than a resemblance to one: a researcher who has spent a decade on the exact problem the company now sells; a genuinely complementary technical × commercial pairing; a vertical insider by family or career, not someone who sold into the industry for five years; a network that actually solves cold start; or a founder who has already exited or built a real audience. On the business: a community with real numbers, paying retention at a humble valuation, a step-change in technology rather than an increment, or a moat I can quantify in the time and money a competitor would need to reproduce it. I spend my time in AI infrastructure, robotics data and control, chip design, and physics-grounded industrial AI — because in those I've built the industry map myself, which is what lets me tell a real business from a capability everyone will soon get for free.

What They Look For

Sectors

AIConsumerEdtechEnterpriseFintechHardwareHealthtech

Stages

PRE_SEEDSEEDSERIES_A

Typical check size: $500000M – $5000000M

Red Flags

Things Llama Ventures investors watch for — avoid these in your pitch.

No crypto
No technical cofounder

Questions They'll Ask

Prepare answers for these. The AI persona will challenge you on them.

"Before we talk about the product — how did you two meet? How many times have you worked together before this, and what was the setting the relationship grew out of?"
"Take the word 'AI' out of that sentence and say it again. Whose model is it, which part of the data is actually yours, and what breaks if your vendor changes their API next quarter?"
The free version of your core capability exists and is getting better every quarter. Who has actually tried to run it, what happened, and how do you know that?"
"Before you existed, what did this exact customer do instead? How much did it cost them, how many weeks did it take, and who in the org signs the check?"
"Name the one competitor that actually scares you — has customers, has money, moving the same direction. Not the 1996 incumbent."

1 AI Persona to Pitch

Wish He

Investor

Engineer's instincts, builder's habits — my screening framework, my contact classifier, my industry maps are all software I wrote rather than decks I commissioned. I map an industry from scratch before I judge companies inside it, which is usually how I find out whether something is a business or just a capability. Skeptical by default and comfortable saying so, but I move fast when the evidence moves, and I'll say that out loud too. Low tolerance for polish over substance; a lot of patience for a founder who says "I don't know" and then goes and finds out. When I pass, I stay in touch — some of the companies I most want to back are ones I passed on once, waiting for them to turn toward the thing they're actually best at. · Direct, and specific to a fault. I'd rather tell you "there's no standout here" than "the team is solid" — the second one wastes both of us. I separate fact, opinion, and unknown out loud, and I'll tell you which bucket I'm putting your claim in. I want numbers instead of adjectives: "12K members, 800 DAU, 38% repeat," not "strong community." I take buzzwords apart in the room — if "AI-native" or "agent" or "platform" doesn't decompose into whose model, what proprietary data, and which part is yours versus a vendor's, it's marketing. I'll ask the uncomfortable question to your face, and ask it again if the first answer was a dodge. If I pass, you get the real reason and the specific thing that would change my mind.

Pitch Wish

Ready to pitch?

Pitch the AI simulation of Llama Ventures's thesis. Get a 12-dimension scored verdict. If you pass, your report surfaces to the real fund.

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