#1
The AI investment that changed how I evaluate every deal now
"I passed on a Series A that returned 40x to another fund. The company was an AI infrastructure play, and I completely misjudged the moat. Here's what that miss taught me."
Why it works
Vulnerability from a credible investor commands attention. Founders and co-investors respect intellectual honesty, and a story framed around a miss positions you as analytical rather than promotional. It also signals what you now look for — attracting founders who fit that updated thesis.
#2
Most AI startups are building features, not companies. Here's how I tell the difference.
"After reviewing 200+ AI pitch decks this year, one pattern keeps emerging: founders confuse a clever GPT wrapper with a defensible business. The distinction is sharper than most people admit."
Why it works
This frames the investor as a rigorous evaluator with high deal flow volume. It publicly articulates an investment thesis, which attracts founders who believe they've built something defensible and want to prove it to you — exactly the quality deal flow VCs need.
#3
5 AI verticals I'm actively tracking for investment in 2025
"Every week I get asked what I'm excited about in AI. So here's an honest, specific answer — not vague categories, but the exact problem spaces where I think the next breakout companies will be built."
Why it works
A specific, forward-looking list is inherently shareable and positions the investor as a thesis-driven operator. It also functions as a public signal to founders in those verticals to reach out, generating direct inbound deal flow from relevant companies.
#4
AI foundation model companies will mostly be zero. Fight me.
"The capital required to compete with OpenAI, Anthropic, and Google at the foundation model layer has effectively closed that market to venture-backable startups. The real alpha is two layers up."
Why it works
A confident, contrarian take from an investor sparks debate in the comments — drawing in other VCs, founders, and operators who agree or push back. This kind of engagement dramatically expands reach and positions the poster as a rigorous independent thinker with a clear thesis.
#5
What's the one AI use case you think is wildly overhyped right now?
"I have my own answer, but I'm genuinely curious what founders and operators in the trenches are seeing. Where is the hype outrunning the actual value creation?"
Why it works
Questions that invite expert opinion drive comment volume from exactly the audience an investor wants to build relationships with — founders and domain experts. The analytical framing signals that this isn't engagement bait but a genuine effort to stress-test market views.
#6
I almost led a round in an AI company that turned out to be mostly demo smoke and mirrors
"The deck was flawless. The demo was impressive. The references were warm. It wasn't until I asked one specific technical question that the whole narrative started to unravel."
Why it works
Diligence war stories are catnip for serious founders who want rigorous investors — and for other VCs comparing notes on evaluation frameworks. Sharing what the red flag was builds credibility and attracts founders who can withstand the same scrutiny.
#7
Why AI gross margins are more complicated than most investors are modeling
"When founders show me 80%+ gross margins on their AI product, I've started asking one follow-up question that makes most of them uncomfortable. The inference cost picture changes significantly at scale."
Why it works
Technical, financially-grounded insights demonstrate operational depth that separates serious investors from generalist observers. This attracts founders who want sophisticated capital partners, and it triggers peer engagement from other investors validating or debating the margin thesis.
#8
7 questions I ask every AI founder before deciding whether to take a second meeting
"Most AI pitch meetings lose me in the first 20 minutes — not because the idea is bad, but because the founder can't answer these seven questions with precision. I've started sharing them upfront."
Why it works
A listicle that doubles as public diligence criteria is extraordinarily useful for founders preparing to fundraise. It positions the investor as transparent and founder-friendly while filtering inbound to only the most prepared teams — a direct deal flow quality improvement.
#9
If you're an AI founder building in a vertical I cover, what's the one question you wish investors would actually ask you?
"I want to be a better investor. And I suspect there's a gap between what most VCs probe in diligence and what actually predicts success in AI businesses. Founders: tell me where we're looking at the wrong things."
Why it works
Inviting founders to critique investor behavior is a powerful trust signal. It generates authentic comment-section engagement from founders, builds relationships before a formal pitch, and provides genuine market intelligence that sharpens the investor's actual diligence process.
#10
The AI companies that will win aren't the ones with the best models — they're the ones with the best data flywheels
"Model quality is table stakes. Distribution is temporary. The only durable moat I see in AI is a data flywheel that compounds with every customer interaction — and almost no one is building for it intentionally."
Why it works
A specific, defensible thesis about competitive advantage in AI sparks high-quality debate among investors and founders alike. It publicly articulates what the investor values, attracting founders who have intentionally architected their data strategy and want a partner who understands why it matters.