Andre Charoo
6.2K posts
@acharoo
GP @MapleVCFund. Venture Partner @Inovia. Executive Fellow @HarvardHBS. First 25 @Uber and @Hired_HQ.
Michael - love to see entrepreneurship spread. To have a vibrant ecosystem, I think you need three independent public companies north of $10b market cap that were founded in region. @Chewy qualifies. Seattle has this, NYC just recently qualified. LA close. It can take a while.
We're hiring a head of ops!! If you have color coded sticky notes and your desk looks like an ikea display please apply <3 <3 Competitive salary, and equity in an early stage venture backed startup. It will be the most fun and challenging job you've ever had.
Exclusive: startup Day AI raised $20M from Sequoia to build the ‘Cursor for CRM’ CEO @markitecht tells @UpstartsMediaCo: “Salesforce as we think of it today is not going to exist in 2 or 3 years. I don’t know if it’ll be us, but I really hope it’s us.” upstartsmedia.com/p/day-ai-sequo…
.@gokulr is one of the most prolific product builders and investors of the last 20 years. He helped build the core ads and product businesses at Google, Facebook, Square, and DoorDash, working directly with many of this generation's best founders and CEOs. He's also invested in more than 700 companies giving him an unusually broad view into how products are built and scaled. Gokul has an incredible ability to give precise and prescriptive advice on how to build products, particularly in AI, and he explains his thinking so clearly that you come away knowing exactly how to apply it. We talk about why judgment is the only thing he believes is truly AI-proof, why Zendesk and Slack are more exposed than Salesforce and NetSuite, and what AI-native startups must do to move customers and their data off legacy systems. We cover everything he's learned from building the most important ads businesses, including the only three ways an ad business can make money, and why ChatGPT may be even more powerful than Google or Facebook for highly targeted ads. He also shares inside stories from Larry and Sergey, Zuck, Jack Dorsey, and Tony Xu, about how each of them approaches product, design, and communication. Enjoy! Timestamps: 0:00 Intro 0:35 The Changing Nature of Product Development 4:09 The Merger of Product and Design 4:54 Managing Non-Deterministic Software 9:06 Judgment: The Future-Proof Human Skill 10:41 Building Durable AI Applications 16:43 The Risk to Legacy Software Companies 21:20 Sources of Stickiness in the Age of AI 23:43 Leadership Lessons from Google 27:41 Learning from Mark Zuckerberg 31:16 Jack Dorsey and the Philosophy of Great Design 35:48 The Product Manager as Editor 40:44 Three Pillars of a Successful Ads Business 49:03 Selecting North Star and Check Metrics 56:04 Hiring Functional Experts for the AI Era 1:00:06 Advice for Managing a Career 1:01:33 Evaluating Founder Authenticity 1:05:20 Best Practices for Board Management 1:11:15 The Kindest Thing
.@reedhastings built Netflix around two ideas that everyone talks about, but are extremely hard to do in practice. The first is finding a simple idea and taking it extraordinarily seriously. Even from Netflix's inception in 1997, Reed explains how the DVD business was always just a bridge to streaming and why the company stayed relentlessly focused on that core model for decades rather than expanding into to new areas. The second is talent density. Netflix intentionally maintained a wide hiring funnel and accepted ~20% first-year attrition. Reed shares how he sustained an exceptionally high bar as the company scaled through the "Keeper Test," paying generous severance packages and guaranteeing employees worked on hard problems alongside other talented people. We explore Netflix's content strategy -- why it treats content like a venture portfolio and how it competes with YouTube -- and what Reed learned from serving on the boards of Microsoft, Meta, Anthropic, and Bloomberg. We also discuss lessons from the Qwikster failure, why Reed believes AI won’t identify the next K-Pop Demon Hunters, and how he's spending his time today focused on education and Powder Mountain. Enjoy! 0:00 Intro 02:00 Talent Density 05:23 Professional Sports Team Model 09:07 Managing on the Edge of Chaos 10:17 The Keeper's Test 12:46 Qwikster 16:30 Contrarian Thinking & Idea Generation 20:04 Lessons from Boards 26:25 The Open Compensation Experiment 28:24 The Strategy Behind Original Content 37:06 AI's Impact on Storytelling 44:53 Power 48:34 Powder Mountain 54:19 AI in Education 57:27 Future Risks & Upside of AI 59:34 The Kindest Thing
.@reedhastings built Netflix around two ideas that everyone talks about, but are extremely hard to do in practice. The first is finding a simple idea and taking it extraordinarily seriously. Even from Netflix's inception in 1997, Reed explains how the DVD business was always just a bridge to streaming and why the company stayed relentlessly focused on that core model for decades rather than expanding into to new areas. The second is talent density. Netflix intentionally maintained a wide hiring funnel and accepted ~20% first-year attrition. Reed shares how he sustained an exceptionally high bar as the company scaled through the "Keeper Test," paying generous severance packages and guaranteeing employees worked on hard problems alongside other talented people. We explore Netflix's content strategy -- why it treats content like a venture portfolio and how it competes with YouTube -- and what Reed learned from serving on the boards of Microsoft, Meta, Anthropic, and Bloomberg. We also discuss lessons from the Qwikster failure, why Reed believes AI won’t identify the next K-Pop Demon Hunters, and how he's spending his time today focused on education and Powder Mountain. Enjoy! 0:00 Intro 02:00 Talent Density 05:23 Professional Sports Team Model 09:07 Managing on the Edge of Chaos 10:17 The Keeper's Test 12:46 Qwikster 16:30 Contrarian Thinking & Idea Generation 20:04 Lessons from Boards 26:25 The Open Compensation Experiment 28:24 The Strategy Behind Original Content 37:06 AI's Impact on Storytelling 44:53 Power 48:34 Powder Mountain 54:19 AI in Education 57:27 Future Risks & Upside of AI 59:34 The Kindest Thing
This is in no doubt an exciting moment for @GarageCapital, but it has to be said that we wouldn’t have been involved if it wasn’t for @mmccauley’s tenacity, conviction and heads down approach to pursuing great opportunities and staying on top of them.
Nvidia acquiring Groq for $20B is a big deal. Groq chips are insanely fast at inference, sometimes 10x GPUs. The trick is to put model weights in SRAM instead of HBM to trade memory capacity for speed. Its $/token may lose to GPUs, but for long-wait inference on models like GPT-5.2 Pro, speed matters. If I were Sam Altman, I’d buy it. $20B is cheap. Jensen made the right call.
