20VC: Why This Time Will Be Worse Than The Great Financial Crisis, Why Down-Rounds, Firesales and Shutdowns Will Happen & The Ultimate Startup Survival Guide; 7 Steps to Ensure Your Company Survives The Storm

Shardul Shah is a Partner at Index Ventures and one of the greatest cyber security investors of the last two decades. Among his many wins, Shardul has led rounds in Datadog, Wiz, Duo Security, Coalition and more. Shardul is also the only Partner investing at Index to have worked in every single Index office from London, to SF, to NYC to Geneva. Prior to Index, Shardul worked with Summit Partners, focusing on healthcare and internet technologies. In Today's Episode with Shardul Shah We Discuss: 1. Investing Lessons from Wiz and Datadog: Why does Shardul believe that TAM (total addressable market) is BS? Why does Shardul believe that every great deal will be expensive? How does Shardul evaluate when to double down and concentrate capital vs when to let someone else come in and lead a round in an existing company? How does Shardul think about when is the right time to sell a position in a company? 2. How the Best VCs Make Decisions: How does Shardul and Index create an environment of truth-seeking together, that is optimised for the best decision-making to take place? What are the biggest mistakes in how VCs make decisions today? Why does Shardul believe that all first meetings should be 30 mins not 60 mins? Why does Shardul believe it is so much harder to make investment decisions when partnerships are remote? What is better remote? 3. The Core Pillars of Venture: Sourcing, Selecting, Securing and Servicing: Which one does Shardul believe he is best at? What is he worst at? Does Shardul believe with the downturn we have moved into a world of selection and not just winning every new deal? Does Shardul believe that VCs provide any value? What are the biggest misnomers when it comes to "VC value add"? 4. Lessons from the Best Investors in the World: Who is the best board member that Shardul sits on a board with? What has Shardul learned from Gili Raanan and Doug Leone on being a good board member? What have been some of Shardul's biggest investing lessons from Danny Rimer? Why does Shardul hate benchmarks when it comes to investing?

16 Sep 202450min

Mike Hudack is the Co-Founder and CEO of Sling, a peer-to-peer payments app whose vision is to simplify the way the world connects financially. Previously, he held roles at Monzo Bank as Chief Product Officer, Deliveroo as Chief Product and Technology Officer, and Facebook where he led ads product and sharing product. In Today's Episode with Mike Hudack We Discuss: 1. Product: Art vs Science: What is the true art of product? What makes the great product leaders and PMs? Is simple always better in product? How do you retain product simplicity with time? When should data be used over intuition in product building? 2. Lessons from Leading Ads at Facebook: What are Mike's single biggest product lessons from building the ads product at Facebook? How did a meeting with Mark Zuckerberg discussing a product change, alter how Mike thinks about product today? What makes Zuck so special on product? What are the biggest mistakes that Facebook made when it came to the ads product? What did they not do that he wishes they had done? 3. Leading Product at Deliveroo: What I Learned: What are Mike's biggest takeaways from his time at Deliveroo on how to make consumer products? What did Deliveroo do from a product perspective that worked so well? What did he learn? What were the single biggest product mistakes that Deliveroo made? What did he learn? How fast do you know when a consumer app is working or not working? When do you go against data and follow your intuition? 4. Building the Biggest Bank in Britain with Monzo: What are Mike's biggest lessons on product building from his time at Monzo? What did Monzo not do that he wishes they had done? Why does Mike think the US is crucial for Monzo? How did Monzo change how Mike thinks about competition? What do you do when your competitor, Revolut, is outshipping you at such a speed?

13 Sep 20241h 6min

David Schneider is a General Partner @ Coatue and one of the great operators of the last 20 years. Prior to Coatue, David was instrumental in ServiceNow's growth to over $100B+ public market value. David led the growth of the company from $100M to $5BN in revenue. Before joining ServiceNow, David held senior positions at Data Domain, the company he joined at $0 in revenue and scaled to $1BN in revenue and an IPO and acquisition. In Today's Episode with David Schneider We Discuss: ServiceNow: Secrets to Scaling to $5BN in ARR: What are David's biggest lessons from scaling ServiceNow to $5BN ARR? What worked? What did not work? What are the most common reasons companies plateau? How did ServiceNow roll out so many different products so effectively? How did David hire and ramp 180 people in 90 days? 2. From OG Operator to Newbie Investor: What have been the single most challenging elements of making the transition to VC? What advice did David get from the biggest names on entering venture? How long did it take David to do his first deal? What advice does he give other operators entering? How does doing deals in 2024 compare to when David started doing deals in 2021? 3. VC Value: Do 90% of VCs Really Damage Companies: Does David agree that 90% of VCs actually detract value? What does David mean when he says that the worst VCs are "seagull VCs"? What are David's biggest tips to founders on how to get the most out of their board? What is enough ownership for David to really give the time needed to a company? 4. Lessons from the Greats: Doug Leone, Bill McDermott, Frank Slootman: Doug Leone: What has David learned from Doug on what it takes to be a great investor and board member? Frank Slootman: What has David learned from Bill on how to be the best leader of a mega company? Bill McDermott: What has David learned from Frank about decision-making and execution.

11 Sep 20241h 6min

Sean Rad is the Founder and former CEO of Tinder. Sean has made more romantic connections between humans than anyone in history with Tinder having matched 50BN different people. Sean is also the Founder of Rad Fund which has made over 100 investments in companies and funds. In Today's Episode with Sean Rad We Discuss: 1. Lessons Scaling Tinder to the Fastest Consumer Social App: Starting: How did the idea for Tinder come to Sean in a restaurant in LA? Scaling: What are Sean's biggest lessons for consumer apps scaling to their first 10,000 users? User Acquisition: How did a party change the entire user acquisition strategy for dinner? What did Tinder not do that Sean wishes they had done? What did Tinder do that with the benefit of hindsight, they should not have done? 2. Leadership Lessons from Tinder CEOship: Annual Product Redesign: Why does Sean believe that every consumer company should have a complete redesign of the app every year? What are the benefits? Detachment: How does Sean advise founders when it comes to detaching their happiness from the performance of the company? What works? What does not work? Common Mistakes: What are the most common mistakes that Sean sees early-stage founders make when it comes to leadership? 3. Money, Wealth and Creating a Family Office: How does Sean analyse his own relationship to money? How has it changed over time? At what stage of wealth does Sean believe you have true financial freedom? What is the single best investment Sean has made? What did he learn? What is the worst investment he has made? What did he learn? What have been the single hardest and most surprising elements of creating a family office? 4. Love, Death, Marriage: In what ways does Sean think love has changed with time? How do we deal with the loneliness pandemic? What does Sean believe are the most non-obvious but important secrets to a happy marriage? How does Sean approach and think about his own spirituality today? Why does he not fear death?

9 Sep 20241h 4min

Nick Chirls is the Founder of Asylum Ventures, a new venture firm dedicated to the creative act of building companies; treating founders like artists, not assets. Asylum raised $55 million to invest $1-2 million in early-stage founders practising the art of making startups. Prior to Asylum, Nick co-founded Notation Capital, one of NYC's most successful pre-seed firms. In Today's Episode with Nick Chirls We Discuss: 1. Why Venture Capital is Broken Today: Why is VC a ponzi scheme today? Why are most VCs sheep and have lost all creativity? Why are most investors today incentivised to get dollars out of the door and not to make great investments? Why are services functions within VC firms total BS? Why do no VCs provide significant enough value to a company that it is needle-moving? 2. How to Make Money in VC in 2024: What are the two ways to make money at seed in 2024? Why do founders in unloved markets care more than those in hot markets? Why will large institutions lose a ton of money investing in the large firms of today? Why does Nick believe VCs should always sell when their founders sell shares? 3. Lessons from 3xing a Fund on One Check: Why does Nick think about not purchasing preferred shares and only buying common shares? Why does Nick believe that investing in competitive markets is stupid? What does Nick believe are the conditions you must accept if you are doing a $5M on $25M seed?

6 Sep 20241h

Zico Colter is a Professor and the Director of the Machine Learning Department at Carnegie Mellon University. His research spans several topics in AI and machine learning, including work in AI safety and robustness, LLM security, the impact of data on models, implicit models, and more. He also serves on the Board of OpenAI, as a Chief Expert for Bosch, and as Chief Technical Advisor to Gray Swan, a startup in the AI safety space. In Today's Episode with Zico Colter We Discuss: 1. Model Performance: What are the Bottlenecks: Data: To what extent have we leveraged all available data? How can we get more value from the data that we have to improve model performance? Compute: Have we reached a stage of diminishing returns where more data does not lead to an increased level of performance? Algorithms: What are the biggest problems with current algorithms? How will they change in the next 12 months to improve model performance? 2. Sam Altman, Sequoia and Frontier Models on Data Centres: Sam Altman: Does Zico agree with Sam Altman's statement that "compute will be the currency of the future?" Where is he right? Where is he wrong? David Cahn @ Sequoia: Does Zico agree with David's statement; "we will never train a frontier model on the same data centre twice?" 3. AI Safety: What People Think They Know But Do Not: What are people not concerned about today which is a massive concern with AI? What are people concerned about which is not a true concern for the future? Does Zico share Arvind Narayanan's concern, "the biggest danger is not that people will believe what they see, it is that they will not believe what they see"? Why does Zico believe the analogy of AI to nuclear weapons is wrong and inaccurate?

4 Sep 20241h

Scott Gorlick was employee #99 at Uber. Over 6 years, Scott built Uber in Atlanta and helped the company scale from 10 cities to $10B in revenue. Scott is also a prolific angel investor having written early checks into Lime and Standard Cognition to name a few. In Today's Episode with Scott Gorlick We Discuss: 1. The Driver Acquisition Playbook: Scaling to 1M Drivers How did Uber acquire 1M drivers? What was the playbook? What worked? What did not work? How much of a role did driver-to-driver referral payments have in driver acquisition? What did Lyft do on the driver acquisition side that Uber should have done? What did the retention look like for drivers on a 30, 60 and 90 day period? 2. The City Expansion Playbook: What was the expansion playbook that Uber used for new cities? What worked in ramping demand in a new city? What did not work? How much of a role did promotions and discounting play? Lessons from them? Why did Uber often let Lyft launch in a new market first? What was the benefit of this? How did Scott see the maturation rate change with new markets opening? How fast did each subsequent market reach profitability? 3. Travis Kalanick and What Uber Could Have Been: How would Uber be different today if Travis was still in charge? What are the biggest mistakes that Dara has made with their M&A strategy? What are some of Scott's biggest leadership lessons from working with Travis? How did Travis create such strong followership and cult around him? What were the single biggest management mistakes made by Travis?

30 Aug 202441min

Arvind Narayanan is a professor of Computer Science at Princeton and the director of the Center for Information Technology Policy. He is a co-author of the book AI Snake Oil and a big proponent of the AI scaling myths around the importance of just adding more compute. He is also the lead author of a textbook on the computer science of cryptocurrencies which has been used in over 150 courses around the world, and an accompanying Coursera course that has had over 700,000 learners. In Today's Episode with Arvind Narayanan We Discuss: 1. Compute, Data, Algorithms: What is the Bottleneck: Why does Arvind disagree with the commonly held notion that more compute will result in an equal and continuous level of model performance improvement? Will we continue to see players move into the compute layer in the need to internalise the margin? What does that mean for Nvidia? Why does Arvind not believe that data is the bottleneck? How does Arvind analyse the future of synthetic data? Where is it useful? Where is it not? 2. The Future of Models: Does Arvind agree that this is the fastest commoditization of a technology he has seen? How does Arvind analyse the future of the model landscape? Will we see a world of few very large models or a world of many unbundled and verticalised models? Where does Arvind believe the most value will accrue in the model layer? Is it possible for smaller companies or university research institutions to even play in the model space given the intense cash needed to fund model development? 3. Education, Healthcare and Misinformation: When AI Goes Wrong: What are the single biggest dangers that AI poses to society today? To what extent does Arvind believe misinformation through generative AI is going to be a massive problem in democracies and misinformation? How does Arvind analyse AI impacting the future of education? What does he believe everyone gets wrong about AI and education? Does Arvind agree that AI will be able to put a doctor in everyone's pocket? Where does he believe this theory is weak and falls down?

28 Aug 202451min