Navigating Personal Challenges in Entrepreneurship

What does it mean to be a “day one partner” for founders—and how does that change in an era of AI-driven acceleration?

On this episode of The Data Minute, Peter sits down with Ed Sim, founding partner of Boldstart Ventures and the voice behind “What’s Hot 🔥 in Enterprise IT VC.” Ed shares what it means to invest at the “inception stage”—before a company is even a company—and how startup formation, team sizes, fundraising expectations, and milestones have radically shifted post-AI boom.

This episode dives into how Boldstart identifies founding teams before they incorporate, why valuation round sizes are splitting into tiny “discovery rounds” and $25M+ jumbo raises, and why the industry is bifurcating between massive multi-stage funds and lean, focused early-stage firms.

Plus: When secondaries make sense, why software moats are elusive, how AI-native investing plays out against pre-AI portfolios, and the surprising thing Ed once did to stop a founder from selling too early. This one’s packed with memorable insights—and honest, founder-first advice.

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Chapters:
00:42 – Inception-stage investing explained
01:50 – The compressed cycles of AI and the speed of change
03:32 – Are we in an AI bubble?
05:00 – Why the "middle class" of startups is disappearing
06:56 – Inception investing vs Pre-seed, Seed, and Series A
08:02 – Finding technical founders before they incorporate
09:34 – Boldstart’s “pure play” approach to being first on the cap table
10:36 – From inception through IPO: Boldstart’s full lifecycle model
11:40 – Snyk as a case study of inception-to-scale investing
12:45 – Discovery vs Classic vs Jumbo inception rounds
14:30 – How fund size determines which deals you can join
15:46 – Why some founders intentionally raise small
16:51 – Raising too much: “The tail wagging the dog”
18:00 – Rethinking how much capital founders actually need
18:57 – Founder pressure and capital: choosing wisely
19:40 – Multi-stage firms at the early stage: Helpful or distorting?
21:15 – Two worlds: legacy portfolio vs AI-native upstarts
22:57 – Superhuman, Clay, and adapting to AI post-launch
25:10 – How early is too early for AI revenue?
26:19 – Moats, stickiness, and workflow depth
27:30 – What makes AI revenue durable?
28:39 – Defining “AI company”: product, workflow, or brand?
30:16 – Is data the new moat?
31:40 – Proprietary data and “day one” data strategy
32:37 – Signs that venture is growing up
33:54 – The bifurcation of venture: massive funds vs focused specialists
35:00 – Why LPs want more true early-stage exposure
36:07 – Lack of liquidity—and how it's changing fund strategy
37:25 – “Venture didn’t beat the S&P” and why that’s the wrong question
38:23 – Secondaries: Which companies actually get them
39:20 – Founder secondaries: removing the stigma
40:42 – Vesting schedules and proactive founder conversations
42:35 – Do we have too many VCs?
43:51 – How to navigate a frothy valuation market
45:28 – Lightning round
51:04 – Outro

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