Ryan Caldbeck
4 statements across 2 episodes · 0 bullish · 3 bearish · 1 people on the record · first statement Feb 12, 2016 by Ryan Caldbeck · said 11 times in 5 episodes since 2016 · across every show →
On the record as a speaker too: Ryan Caldbeck's record, appearances and statements → this page counts the times other people say the name.
Mentions by year
brought up most by Harry Stebbings (9), Fred Destin (2)
tap a year for its mentions
2019 4 mentions in 2 episodes 2 per episode
- 20VC: What It Takes To Be The Most Effective Coach To Startup Founders, The Biggest Surprises and Challenges About Transitioning To Venture From Operations & 3 Trends Shaking The World of Consumer Today with Victoria Treyger, General Partner @ Felicis Ven
- 20VC: Stride's Fred Destin on The Acceptable vs Non-Acceptable Risks When Investing, How Startup Founders Can Improve The Quality of Their Decision-Making and Must Play for Batting Average & Why Plans Do Not Matter and No Board Member Should Bash An Entre
- every mention in 2019, scene by scene →
2018 1 mention in 1 episode
2016 6 mentions in 2 episodes 3 per episode
- 20VC: Binary's Justin Caldbeck on Specialisation Being Key To Success In VC, The Biggest Problem In VC Today and Why The Future Is A Moneyless Barter Economy
- 20VC: Why Adaptability Is Key To Fundraising, Why Supply Is Always The First Priority In Marketplaces & Why Sometimes You Have To Hire Fast & Fire Fast with Nav Athwal, Founder & CEO @ RealtyShares
- every mention in 2016, scene by scene →
Everything said about Ryan Caldbeck, oldest first
Feb 12, 2016 negative
Nov 2, 2018
Once you start considering firing someone, the decision is made
“When you're thinking about whether or not to let someone go, to fire someone, the decision's already made, and that point is absolutely true. When you're having that consideration in your head, it has gone far enough, and you need to just make the decision.”
Nov 2, 2018 negative
Quantitative VC models lack historical training data for early tech
“There weren't a thousand other examples where there's enough training data to train models to understand what success looks like. There might be two or four. That's a reason why I really struggle to understand how this could be successful in tech, but in other…”