Almost every fund eventually has a company raise money again at a lower valuation than before – a “down round” – or fail to raise at all. That’s just how startup investing works, not a sign anyone did anything wrong. What actually helps in that moment is being able to look back at your own reasoning clearly. And keeping that
The Follow-On Decision Nobody Wants to Own When a company you’ve already invested in comes back asking for more money – a “follow-on” – that decision usually has to happen fast, often in the middle of everything else you’re juggling. It’s not that funds don’t want to plan ahead here. It’s that planning ahead means knowing exactly how every company
The Power Law Lie GPs Tell Themselves Venture returns genuinely follow a power law – a small share of deals drive most of the gains. But funds routinely misuse that fact to excuse loose screening, on the logic that “one winner covers everything, so early diligence doesn’t need to be rigorous.” The data on who actually wins under the power
Why Your Best LP Update Is Boring The LP updates that build the most trust over time aren’t the ones with the most exciting news – they’re the ones with the most predictable structure. Same format, same cadence, no surprises buried in paragraph four. That consistency is a proxy LPs read for something bigger: whether the fund behind the update
Why “We’re Building an AI-Native Fund” Is Becoming Part of the Fundraising Pitch “AI-native” used to be an internal ops decision – which tools the team happened to use. It’s increasingly showing up inside the fundraising pitch itself, because LPs are starting to read a fund’s AI infrastructure as a proxy for speed, consistency, and reporting quality. The risk is
Why Emerging Managers Are Winning LP Trust With Better Systems, Not Just Better Numbers Institutional LPs are concentrating capital with established firms and putting first-time managers through operational due diligence that goes far beyond thesis and track record – data rooms, decision logs, reporting cadence. The emerging managers actually closing capital right now aren’t out-pitching the big funds; they’re out-organizing
The Anti-Portfolio Problem: What Your Fund’s Passes Say About Its Judgment Every fund has an anti-portfolio – the deals it passed on that later became huge wins for someone else. Bessemer famously publishes theirs. Most funds never study their own, because reviewing a pass takes deliberate effort and reviewing a win doesn’t. That’s backwards: passes are the highest-signal dataset a
The Deal Champion’s Blind Spot: Why the Person Who Loves a Deal Can’t Be the One Who Clears It The partner who sourced a deal and wrote the memo has already invested identity and reputation in it before the IC meeting starts – which makes them structurally unable to grade it neutrally, no matter how disciplined they are. Formally assigning
I asked Claude to help me build a pitch deck. Here’s what actually happened. We had a meeting with a potential client. 400+ person company. Fortune-level clients. Three established practices. I did the prep. I used Claude. The deck was solid. And then the client redirected us, mid-meeting. Not because the deck was bad. Because they were further along
Will AI Take My Job? I have 4 years of experience in IT services analysis. Last month I asked #Claude directly: will you take my job? It said no. Gave me a thoughtful answer about human judgment, relationship intelligence and contextual reasoning. Very reassuring. Very well structured. Then I asked it to analyze Wipro’s last quarterly filing. It took 3
Before we start working with a services founder, there are a few questions I always ask. Not a formal framework, just things I’ve learned to check over a few hundred conversations. They’re not trick questions. Most founders have thought about at least some of them. But the pattern I keep seeing is this: the founders who’ve actually broken past $10M
One Anthropic employee generates the same revenue as 180 Tata Consultancy Services employees. Yes, you read that right. We were looking at revenue-per-employee numbers across a few “tech companies” this week for a client that wanted to benchmark itself against the best in the industry. When we extended that to the top AI companies – the spread is just crazy.
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