GROWTH FOUNDERS: HIRE A NEAR-PEER A portfolio founder approaching $100M in ARR asked me about the single biggest and most impactful hire they can make. I thought for a bit tand said: "Hire a near-peer". In every generational company I've been part of, the founders hired a near-peer, who was essential to the company's success. - Google: Larry and Sergey hired @ericschmidt . - Facebook: Mark hired @sherylsandberg . - Square: Jack hired @rabois . - DoorDash: Tony hired @chrispa - Coinbase: Brian hired @emiliemc Characteristics of a near-peer: 1. They're so good that the founder will be fine reporting to them if the roles were reversed. (Mark has said publicly that he'd be fine reporting to Sheryl) 2. Their strengths perfectly complement the founder's strengths; however, they share many cultural attributes with the founder and pass the founder's airport test, since the founder will be spending a ton of time with them (example: Eric being a Computer Scientist, which was culturally very important at Google back in the day) 3. They're systems builders who have already operated at the scale you're growing into. Pattern recognition on $100M to $1B is not something you build in real-time. A near-peer lets the founder focus on what only the founder can do (product, vision, culture). The near-peer handles everything else. Also, near-peers stay for the long haul. Decade-plus tenure is standard. The reason this hire matters more than any other: after $50M ARR, the bottleneck shifts from product-market fit to organizational scale. The founder is still the visionary. But the company needs someone who has already scaled a company of that size. Most founders wait too long. They hire functional VPs first (Sales, Marketing, Engineering, Finance) and hope the collective covers the gap. It rarely does. A stack of VPs reporting into a founder who has never scaled a company creates coordination overhead, and the founder becomes the bottleneck. The near-peer absorbs that overhead. They turn the founder's vision into daily operating decisions. They give the VPs a leader who has actually run a company this size. Growth founders: if you're between $50M and $300M in ARR and every week feels like a coordination tax, the near-peer is the hire that determines whether you build a $10-100B company or top out at $1B. Start the search now, ideally through warm intros from your venture/angel investors and advisors.

Top 20 Startups by Web Traffic founded since 2020 1. DeepSeek 2. Perplexity 3. Suno 4. Polymarket 5. Gamma 6. ElevenLabs 7. Lovable 8. Arena 9. xAI 10. Supabase 11. Manus 12. Higgsfield 13. Cursor 14. Fanvue 15. OpenRouter 16. GPTZero 17. Genspark 18. ShopMy 19. Venice 20. Whop Some interesting observations: — Only 25% were not AI: Polymarket, Supabase, Fanvue, ShopMy, Whop — 20% were acquired — Startups that didn't surprisingly didn't make the cut: Kalshi (founded 2018), Mistral (10M), OpenEvidence (11.4M), Cognition — All but 2 are unicorns (GPTZero, Fanvue), 7 decacorns, but there's no clear correlation between traffic and valuation

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

$META is reportedly developing a cloud business to sell access to excess AI compute, per Bloomberg. The internal initiative is called Meta Compute. The plans being considered: AI model access hosted on Meta infrastructure, similar to AWS Bedrock Raw AI compute capacity, closer to CoreWeave Developer access to Meta’s data centers, chips, and models

Vertically integrating the full inference hardware stack is extremely hard. Hear @UbertiGavin and @robertwachen share three years of battle stories leading into today's launch

Take small businesses for example. They are less likely to use AI in the first place, but when they do, they use it more intensely. They may not have a dedicated engineering or finance team, so AI can unlock new revenue streams that previously required higher upfront investment. Ultimately, I think this is the mechanism by which firms increase headcount upon AI adoption, even though AI presumably fulfills new and existing tasks for their teams. They can go do more things now.