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Meet Alexandr Wang. His resume at 19: • Pro coder since 15 • Machine learning engineer at Quora • Solved AI problems PhD holders couldn't But Wang wasn't satisfied. He saw a billion-dollar opportunity others missed:

Baptiste

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384,876 次观看 • 1 年前 •via X (Twitter)

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Baptiste1 年前

This is the youngest billionaire you've never heard of. At 19, he dropped out of college. Today? Nvidia, Meta, and the US government can't survive without him. Here's how a teenager built a company the entire world depends on:

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Baptiste1 年前

The AI boom was in full swing. • Alexa was in millions of homes • Self-driving cars were hitting the streets • ChatGPT was on its way But there was a massive problem no one was talking about. Wang spotted it. And he was about to build an empire around the solution…

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Baptiste1 年前

The issue? Data. AI models were starving for high-quality, labeled data. But it’s a tedious, error-prone, and expensive nightmare. It was the invisible bottleneck choking AI's potential. Wang's solution?

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Baptiste1 年前

Turn data labeling into a global, AI-powered machine. In 2016, at 19, he co-founded Scale AI. Their revolutionary approach: • Global workforce for data labeling • AI enhances human work This human-AI combo set Silicon Valley on fire:

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Baptiste1 年前

By 2019, Scale AI hit unicorn status. Wang was just 22. Scale AI's client list is wild: • Uber • Pinterest • OpenAI ... the list goes on. But Wang wasn't done. He set his sights on an even bigger target...

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Baptiste1 年前

The U.S. government. In 2020, the Department of Defense came asking for help with: • Satellite imagery analysis • Drone footage processing • Predictive maintenance for vehicles Suddenly, Wang’s tech was safeguarding national security:

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Baptiste1 年前

As AI grips the world, Scale AI has become indispensable: • Training Tesla's cars to outsmart human drivers • Helping Nvidia see the world in 1s and 0s • Keeping Meta's feed clean of digital toxins 300+ top companies now relied on Wang's magic. Scale AI's new valuation?

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Baptiste1 年前

$14.3 billion. By 2024, Wang turned 27. His net worth: $2 billion. But money wasn't the goal. Impact was. Wang's vision was to accelerate humanity's progress through AI. How? By tackling some of the world's most pressing challenges:

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Baptiste1 年前

Scale AI began working on: • Climate change models • Drug discovery • Disaster response optimization The question wasn't if AI would change the world. It was how fast. And Wang was determined to push the boundaries. His journey teaches us:

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Baptiste1 年前

1. Spot invisible problems 2. Scale solutions globally 3. Aim for world-changing impact The next big idea isn't in a lab. It's with someone ready to drop everything and build it. But here's the most exciting part:

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Baptiste1 年前

The AI revolution is just beginning. As you read this, Wang's tech is: • Making roads safer • Protecting national security • Accelerating breakthroughs It started with a teen who dared to think differently. But Wang's story isn't just about one exceptional individual...

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Baptiste1 年前

It's about the power of bold ideas and relentless execution. The next world-changing innovation could come from anywhere. It could come from you…

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Baptiste1 年前

So, what's your big idea? What problem do you see others overlooking? The world is waiting for solutions. And you have the power to create them. Ready to be part of the revolution?

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Baptiste1 年前

If so... • Join our 300K+ community • Follow @BaptisteParravicini for more on APIs, tech, and the future of AI. • Repost for your network if you found it helpful! ♻️ The future is connected. Become part of it...

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Baptiste1 年前

I am Baptiste Parravicini: • Tech entrepreneur & API visionary • Co-founder of apidays, world's leading API conference • Passionate about AI integration & tech for the greater good Follow for insights on how APIs are connecting our future Repost for your network 🔄:

相关视频

Alexandr Wang (Alexandr Wang) started Scale AI to help machine learning teams label data faster. It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from. Today, that early idea powers a multi-hundred-million-dollar engine behind America's AI infrastructure—fueling everything from Fortune 500 workflows to real-time military planning. Just last week, Meta agreed to invest over $14 billion in Scale, valuing the company at $29 billion. Alexandr joined us on Lightcone Podcast to share how Scale evolved from a scrappy YC startup into the backbone of some of the world's most advanced AI systems, how he thinks about competition with Chinese AI labs, and what it takes to build infrastructure that shapes the frontier. 01:15 - Alexandr’s early days at YC 07:25 - Dialing in on what worked 10:24 - Model improvements, evals 19:18 - The techno-optimist view of work 27:47 - The turning points for Scale AI 37:37 - Agentic workflows 41:55 - “Humanity’s Last Exam” 47:48 - U.S. vs China in AI and hard tech 56:57 - How to be hardcore

Alexandr Wang (Alexandr Wang) started Scale AI to help machine learning teams label data faster. It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from. Today, that early idea powers a multi-hundred-million-dollar engine behind America's AI infrastructure—fueling everything from Fortune 500 workflows to real-time military planning. Just last week, Meta agreed to invest over $14 billion in Scale, valuing the company at $29 billion. Alexandr joined us on Lightcone Podcast to share how Scale evolved from a scrappy YC startup into the backbone of some of the world's most advanced AI systems, how he thinks about competition with Chinese AI labs, and what it takes to build infrastructure that shapes the frontier. 01:15 - Alexandr’s early days at YC 07:25 - Dialing in on what worked 10:24 - Model improvements, evals 19:18 - The techno-optimist view of work 27:47 - The turning points for Scale AI 37:37 - Agentic workflows 41:55 - “Humanity’s Last Exam” 47:48 - U.S. vs China in AI and hard tech 56:57 - How to be hardcore

Y Combinator

396,950 次观看 • 1 年前

Leigh Marie Braswell (joined Scale AI at 3 employees) shares Alexandr Wang's vision & intuition: "Alex & his ability to put himself in the right rooms, know the right people, & be able to see.. where's all the revenue going to come from? From the next wave of what's going on in machine learning, human intelligence & data will always be needed. And I have full confidence, because he's done it time & time again, that Alex is always going to be able to figure out where is that place where Scale needs to build a product to sort of be able to bring the humans to the models." "Leigh Marie" Braswell Alexandr Wang Scale AI

Leigh Marie Braswell (joined Scale AI at 3 employees) shares Alexandr Wang's vision & intuition: "Alex & his ability to put himself in the right rooms, know the right people, & be able to see.. where's all the revenue going to come from? From the next wave of what's going on in machine learning, human intelligence & data will always be needed. And I have full confidence, because he's done it time & time again, that Alex is always going to be able to figure out where is that place where Scale needs to build a product to sort of be able to bring the humans to the models." "Leigh Marie" Braswell Alexandr Wang Scale AI

Molly O’Shea

66,174 次观看 • 1 年前

Thomas Peterffy never read a single investment book - just borrowed $100 from his father and built the world's best $110 billion brokerage firm he finally opened up about the one principle behind every trade he's ever made "every two cents the stock goes down, I buy a thousand shares - then sell as it climbs - I prefer trading profits to position profits" he landed in New York at 21 with no English - "learning to code was easier than learning the language" - so he let computers do what he couldn't, and built the first machine that traded on its own Goldman Sachs came knocking with a $900 million offer - he turned it down -then said "$3 billion" just to end the conversation - today his firm is worth over $110 billion on the AI bubble: "we're not there yet - but we will be AI works where behavior can be predicted - molecules, atoms - general intelligence is still far away" bookmark & watch ↓

Thomas Peterffy never read a single investment book - just borrowed $100 from his father and built the world's best $110 billion brokerage firm he finally opened up about the one principle behind every trade he's ever made "every two cents the stock goes down, I buy a thousand shares - then sell as it climbs - I prefer trading profits to position profits" he landed in New York at 21 with no English - "learning to code was easier than learning the language" - so he let computers do what he couldn't, and built the first machine that traded on its own Goldman Sachs came knocking with a $900 million offer - he turned it down -then said "$3 billion" just to end the conversation - today his firm is worth over $110 billion on the AI bubble: "we're not there yet - but we will be AI works where behavior can be predicted - molecules, atoms - general intelligence is still far away" bookmark & watch ↓

bodila

89,884 次观看 • 21 天前

A quant in Shanghai pissed me off. Enough that I copied his bot and pulled +$2,200 out of it in 4 days. And the dumb part is he isn't doing anything clever. I found a wallet I couldn't look away from. At first I figured it was a broken bot, since it just repeats the same move nonstop. Only 5-minute BTC markets, 24,000 trades since March, and it's up around $75k in 4 months. For a solid 20 minutes I couldn't find the catch. It looked way too dumb to make that kind of money. So I ran it through Claude to reverse-engineer every trade, and then I saw what it does. It buys cheap BTC at 13, 19, 20 shares and sells at a dollar. The whole thing rides on the asset being underpriced for a moment. His Polymarket account is nobebot if you want to look it over yourself. And you know what gets me the most? This is dumber than every indicator, MACD, RSI, and oscillator setup the whole CT runs on. And it still works. The math is stupid simple. Buy at 15-20 cents, sell at a dollar, and that's +300-600% on a single trade. At those numbers you only need to be right on one trade out of four to stay green. Half his trades burn to zero, but over the long run the edge covers the losses.

A quant in Shanghai pissed me off. Enough that I copied his bot and pulled +$2,200 out of it in 4 days. And the dumb part is he isn't doing anything clever. I found a wallet I couldn't look away from. At first I figured it was a broken bot, since it just repeats the same move nonstop. Only 5-minute BTC markets, 24,000 trades since March, and it's up around $75k in 4 months. For a solid 20 minutes I couldn't find the catch. It looked way too dumb to make that kind of money. So I ran it through Claude to reverse-engineer every trade, and then I saw what it does. It buys cheap BTC at 13, 19, 20 shares and sells at a dollar. The whole thing rides on the asset being underpriced for a moment. His Polymarket account is nobebot if you want to look it over yourself. And you know what gets me the most? This is dumber than every indicator, MACD, RSI, and oscillator setup the whole CT runs on. And it still works. The math is stupid simple. Buy at 15-20 cents, sell at a dollar, and that's +300-600% on a single trade. At those numbers you only need to be right on one trade out of four to stay green. Half his trades burn to zero, but over the long run the edge covers the losses.

may.crypto {🦅}

14,767 次观看 • 1 个月前

Alexandr Wang on why Paul Graham’s “Schlep Blindness” essay was seminal for Scale AI “One of the secrets to Scale AI — and I think this applies to almost every industry — was that the problem we were solving of building really high quality data sets was something that most machine learning teams knew was very important but it wasn’t necessarily the sexiest problem that every AI scientist wanted to spend their days and nights working on.” Alexandr continues: “There was one article that was pretty seminal for me early on. It was an essay by Paul Graham called ‘Schlep Blindness.’ I’d encourage everyone to read it if you get a chance. But basically the idea was that most people avoid thinking about the really difficult, hairy, ugly, and annoying problems that exist in the world but they’re really important. He actually uses Stripe as one of the examples in his essay, but these problems are everywhere. The ugly, hairy problems that everyone knows are important but aren’t sexy to work on — if you can identify what those problems are, they generally make really exciting startup ideas.” This was a lot of the original pitch for Scale: “You know this is important but you probably aren’t the most excited to work on it.” And then the early Scale team was super scrappy, which helped them earn the trust of their customers: “They saw our product velocity and how fast we were moving. They thought to themselves, ‘Even if they don’t have the perfect product today, they’re going to get to a product that we’re going to be able to rely on really quickly.’” Source: Startup Grind (Apr 2022)

Alexandr Wang on why Paul Graham’s “Schlep Blindness” essay was seminal for Scale AI “One of the secrets to Scale AI — and I think this applies to almost every industry — was that the problem we were solving of building really high quality data sets was something that most machine learning teams knew was very important but it wasn’t necessarily the sexiest problem that every AI scientist wanted to spend their days and nights working on.” Alexandr continues: “There was one article that was pretty seminal for me early on. It was an essay by Paul Graham called ‘Schlep Blindness.’ I’d encourage everyone to read it if you get a chance. But basically the idea was that most people avoid thinking about the really difficult, hairy, ugly, and annoying problems that exist in the world but they’re really important. He actually uses Stripe as one of the examples in his essay, but these problems are everywhere. The ugly, hairy problems that everyone knows are important but aren’t sexy to work on — if you can identify what those problems are, they generally make really exciting startup ideas.” This was a lot of the original pitch for Scale: “You know this is important but you probably aren’t the most excited to work on it.” And then the early Scale team was super scrappy, which helped them earn the trust of their customers: “They saw our product velocity and how fast we were moving. They thought to themselves, ‘Even if they don’t have the perfect product today, they’re going to get to a product that we’re going to be able to rely on really quickly.’” Source: Startup Grind (Apr 2022)

Startup Archive

56,503 次观看 • 2 个月前

Maths savant turned Cognition founder Scott Wu joins me to discuss their AI software engineer, acquiring Windsurf over a weekend, the Moneyball-ification of everything, math contests with Alexandr Wang in 6th grade, the future of independent coding tools, and why he thinks we already have AGI. Subscribe on Spotify, Apple Podcasts, and YouTube for future Cheeky Pint episodes. Timestamps: 00:00 Intro 01:13 Early life and maths competitions 03:47 Addepar job as a high schooler 05:43 Where are all the young founders? 08:45 Moneyball-ification of everything 11:42 Cognition’s AI software engineer, Devin 15:46 Essential and accidental complexity 17:59 How Devin works with enterprises 19:48 IDE productivity 21:56 Nihilist computer use argument 25:55 Benchmarking Devin 27:15 Market structure 30:32 Agent economy 37:21 Cognition’s team of founders 39:31 Jevons paradox and software 42:00 When will we see AI UIs? 45:52 “I think we have AGI” 47:03 Windsurf deal 52:37 M&A in AI 54:21 Cognition’s culture 55:48 Learning as a CEO 57:12 Scott’s information diet

Maths savant turned Cognition founder Scott Wu joins me to discuss their AI software engineer, acquiring Windsurf over a weekend, the Moneyball-ification of everything, math contests with Alexandr Wang in 6th grade, the future of independent coding tools, and why he thinks we already have AGI. Subscribe on Spotify, Apple Podcasts, and YouTube for future Cheeky Pint episodes. Timestamps: 00:00 Intro 01:13 Early life and maths competitions 03:47 Addepar job as a high schooler 05:43 Where are all the young founders? 08:45 Moneyball-ification of everything 11:42 Cognition’s AI software engineer, Devin 15:46 Essential and accidental complexity 17:59 How Devin works with enterprises 19:48 IDE productivity 21:56 Nihilist computer use argument 25:55 Benchmarking Devin 27:15 Market structure 30:32 Agent economy 37:21 Cognition’s team of founders 39:31 Jevons paradox and software 42:00 When will we see AI UIs? 45:52 “I think we have AGI” 47:03 Windsurf deal 52:37 M&A in AI 54:21 Cognition’s culture 55:48 Learning as a CEO 57:12 Scott’s information diet

John Collison

345,829 次观看 • 11 个月前

A Chinese mathematician posted a 3 minute video on Bilibili explaining how he lost his $10,000 a month gig to AI. The model he had been training started writing harder math problems than he could invent. He admitted his own mistake in business positioning. He had spent four years hand writing PhD level math problems for Scale AI's reinforcement learning pipeline. $50 to $100 per problem. 200 problems a month. Then synthetic data killed his entire contract category. He was no longer able to invent a problem the machine could not solve. At 2:13 he says the word agent. He says it once. He never says it again in the video. The way he says it is the only thing on screen that did not come off the teleprompter. He has been recording videos off a teleprompter for three months. The teleprompter runs on the same agent that killed his Scale AI work. Every script is generated by Claude. Every word he reads to camera is the agent's. The new job is reading. Someone pulled the script repository from a Cursor instance the dev had left public. The folder was labeled bilibili-laments. Inside were 47 video scripts. All in his voice. All written by Claude. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. The mathematician had been one of them. He had wired the agent into his content pipeline the week Scale AI cut him off. He had been a PhD candidate at one of the top five Chinese math schools. He taught there for two years before going full time on Scale AI contracts. He still has the credentials. He still has the office. He just no longer writes anything. He wanted to show people how AI took his career. He accidentally showed them how AI also took his post mortem.

A Chinese mathematician posted a 3 minute video on Bilibili explaining how he lost his $10,000 a month gig to AI. The model he had been training started writing harder math problems than he could invent. He admitted his own mistake in business positioning. He had spent four years hand writing PhD level math problems for Scale AI's reinforcement learning pipeline. $50 to $100 per problem. 200 problems a month. Then synthetic data killed his entire contract category. He was no longer able to invent a problem the machine could not solve. At 2:13 he says the word agent. He says it once. He never says it again in the video. The way he says it is the only thing on screen that did not come off the teleprompter. He has been recording videos off a teleprompter for three months. The teleprompter runs on the same agent that killed his Scale AI work. Every script is generated by Claude. Every word he reads to camera is the agent's. The new job is reading. Someone pulled the script repository from a Cursor instance the dev had left public. The folder was labeled bilibili-laments. Inside were 47 video scripts. All in his voice. All written by Claude. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. The mathematician had been one of them. He had wired the agent into his content pipeline the week Scale AI cut him off. He had been a PhD candidate at one of the top five Chinese math schools. He taught there for two years before going full time on Scale AI contracts. He still has the credentials. He still has the office. He just no longer writes anything. He wanted to show people how AI took his career. He accidentally showed them how AI also took his post mortem.

Carver

11,906 次观看 • 2 个月前

19 years ago, one of the world's top hurricane forecasters took a stand against climate orthodoxy. Dr. William Gray, Professor Emeritus at Colorado State, is on record saying, "I am of the opinion this is one of the greatest hoaxes ever perpetrated on the American people." Gray wasn't fringe. He was respected, prolific, and data-driven. But by 2006, he was cast out. Not for bad science, but for refusing to parrot the CO2 dogma. He accused alarmists of cherry-picking data, exaggerating models, and silencing dissent. "They argue not as scientists, but as lawyers," he said. The establishment responded with smears, defunding, and exclusion. His career, erased. The climate machine is a trillion-dollar industry powered by fear, models, and ideology. But real-world data keeps defying the script. Gray didn't live to see its full unraveling, but he saw through its machinery. And the collapse is coming.

19 years ago, one of the world's top hurricane forecasters took a stand against climate orthodoxy. Dr. William Gray, Professor Emeritus at Colorado State, is on record saying, "I am of the opinion this is one of the greatest hoaxes ever perpetrated on the American people." Gray wasn't fringe. He was respected, prolific, and data-driven. But by 2006, he was cast out. Not for bad science, but for refusing to parrot the CO2 dogma. He accused alarmists of cherry-picking data, exaggerating models, and silencing dissent. "They argue not as scientists, but as lawyers," he said. The establishment responded with smears, defunding, and exclusion. His career, erased. The climate machine is a trillion-dollar industry powered by fear, models, and ideology. But real-world data keeps defying the script. Gray didn't live to see its full unraveling, but he saw through its machinery. And the collapse is coming.

Electroverse

40,704 次观看 • 1 个月前

Alexandr Wang, head of Meta Superintelligence Labs: "A decade ago, the best AI models could recognize cats in YouTube videos. And now we're talking to a digital god." He said it in passing, as a warm-up to his real point. This is the man Meta paid 14.3 billion dollars to get. He started the company at 19. His real point is the part worth stopping on. He says the whole argument about when superintelligence arrives, two years or five, is close to a waste of time. Not because he thinks it is far off. Because he thinks the answer changes nothing. Then this. For the entire history of humanity, the bottleneck on progress was getting a group of smart people together and pointed at one goal. That is what is about to stop being scarce. "The scarce resource isn't going to be intelligence or agency. I really think it's going to be vision and ambition." Read that as the job description it is. When thinking becomes cheap, the only expensive thing left is knowing what to think about. Most people are bracing for a world where they have to be smarter. He is describing a world where being smarter is free, and having a reason is not.

Alexandr Wang, head of Meta Superintelligence Labs: "A decade ago, the best AI models could recognize cats in YouTube videos. And now we're talking to a digital god." He said it in passing, as a warm-up to his real point. This is the man Meta paid 14.3 billion dollars to get. He started the company at 19. His real point is the part worth stopping on. He says the whole argument about when superintelligence arrives, two years or five, is close to a waste of time. Not because he thinks it is far off. Because he thinks the answer changes nothing. Then this. For the entire history of humanity, the bottleneck on progress was getting a group of smart people together and pointed at one goal. That is what is about to stop being scarce. "The scarce resource isn't going to be intelligence or agency. I really think it's going to be vision and ambition." Read that as the job description it is. When thinking becomes cheap, the only expensive thing left is knowing what to think about. Most people are bracing for a world where they have to be smarter. He is describing a world where being smarter is free, and having a reason is not.

Casper

51,239 次观看 • 9 天前

Faker talked about how supportive his father was in his dream of becoming a pro gamer 🥺❤️ 🐧: Yes, it seems so. Looking back on it now, it couldn't have been easy to support and back me like that. My father, from what I’ve heard, originally wanted to be a baseball player. However, he ended up doing something else instead. 🐧: I’ve heard, not directly from him, but from others, that because he had that lingering regret in his heart, he wanted to support me [in my dreams] more. So, I’m very grateful for that. 🐧: Having him support me so that I could work hard at what I wanted to do... as you said, it probably wasn't easy during that time. 🤵🏻‍♂️: Indeed.

Faker talked about how supportive his father was in his dream of becoming a pro gamer 🥺❤️ 🐧: Yes, it seems so. Looking back on it now, it couldn't have been easy to support and back me like that. My father, from what I’ve heard, originally wanted to be a baseball player. However, he ended up doing something else instead. 🐧: I’ve heard, not directly from him, but from others, that because he had that lingering regret in his heart, he wanted to support me [in my dreams] more. So, I’m very grateful for that. 🐧: Having him support me so that I could work hard at what I wanted to do... as you said, it probably wasn't easy during that time. 🤵🏻‍♂️: Indeed.

안개새벽 𓏲ּ𝄢

26,445 次观看 • 7 个月前

Kelsey Hightower has one of the most inspiring stories in tech: he went from a technician installing DSL modems, through self-directed study and very hard work, to one of the very few Distinguished Engineer at Google whom Satya Nadella personally persuaded to join Microsoft. Timestamps: 00:00 Intro 03:34 Kelsey’s first job at McDonald’s 05:04 His non-traditional path into tech 11:45 Landing his first tech job with an A+ certification 15:33 His entrepreneurial years 19:45 Joining Google as a data center technician 27:48 Learning automation at a Rackspace spinoff 33:26 Moving into financial services 50:00 Building a reputation through open source 53:55 From configuration management to containers 1:08:20 The rise of Kubernetes 1:25:05 Why he almost joined NASA instead of Google 1:29:20 Defining DevRel at Google 1:38:20 Demonstrating impact at Google 1:41:20 Microsoft's offer 1:55:20 Learning how to slow down 2:06:39 Advising and investing 2:15:03 A people-first view of GenAI 2:24:27 Using AI with guardrails 2:28:26 Matching AI to the task 2:36:06 Staying relevant in the AI era Brought to you by outstanding teams building products I love: • Antithesis: verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry: application monitoring software considered “not bad” by millions of developers • Buildkite: CI software built to absorb whatever your coding agents throw at the build queue. OpenAI, Anthropic, Uber and others are customers: Three interesting learnings from Kelsey: 1. Side hustles and doing your own thing teach you business like no IC job can. Before becoming a software engineer at Google, Kelsey was a manager for his comedian friend, operated a computer store, and did IT contracting. These gigs taught him logistics, planning, and about money. All this helped him be far more effective at talking with executives and acting as an executive sponsor inside Google. 2. Can you explain what your startup does without mentioning AI? When Kelsey researches startups seeking his advice, he challenges founders to not say “AI” once. This means that they must explain the actual value their company creates. One unexpected benefit of this is that it often reveals there are easier, cheaper ways to achieve a goal than with AI. 3. It’s very rare to get an extra zero put on your compensation figure – but it happened. Kelsey was a successful, well-paid Google engineer when Microsoft made him an offer that 10x’d his salary (!!). When Kelsey told Google he was planning to take the offer, it matched the offer, proving that his market value had massively increased. It shows that being well paid doesn’t necessarily mean you’re being paid at the correct market rate.

Kelsey Hightower has one of the most inspiring stories in tech: he went from a technician installing DSL modems, through self-directed study and very hard work, to one of the very few Distinguished Engineer at Google whom Satya Nadella personally persuaded to join Microsoft. Timestamps: 00:00 Intro 03:34 Kelsey’s first job at McDonald’s 05:04 His non-traditional path into tech 11:45 Landing his first tech job with an A+ certification 15:33 His entrepreneurial years 19:45 Joining Google as a data center technician 27:48 Learning automation at a Rackspace spinoff 33:26 Moving into financial services 50:00 Building a reputation through open source 53:55 From configuration management to containers 1:08:20 The rise of Kubernetes 1:25:05 Why he almost joined NASA instead of Google 1:29:20 Defining DevRel at Google 1:38:20 Demonstrating impact at Google 1:41:20 Microsoft's offer 1:55:20 Learning how to slow down 2:06:39 Advising and investing 2:15:03 A people-first view of GenAI 2:24:27 Using AI with guardrails 2:28:26 Matching AI to the task 2:36:06 Staying relevant in the AI era Brought to you by outstanding teams building products I love: • Antithesis: verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry: application monitoring software considered “not bad” by millions of developers • Buildkite: CI software built to absorb whatever your coding agents throw at the build queue. OpenAI, Anthropic, Uber and others are customers: Three interesting learnings from Kelsey: 1. Side hustles and doing your own thing teach you business like no IC job can. Before becoming a software engineer at Google, Kelsey was a manager for his comedian friend, operated a computer store, and did IT contracting. These gigs taught him logistics, planning, and about money. All this helped him be far more effective at talking with executives and acting as an executive sponsor inside Google. 2. Can you explain what your startup does without mentioning AI? When Kelsey researches startups seeking his advice, he challenges founders to not say “AI” once. This means that they must explain the actual value their company creates. One unexpected benefit of this is that it often reveals there are easier, cheaper ways to achieve a goal than with AI. 3. It’s very rare to get an extra zero put on your compensation figure – but it happened. Kelsey was a successful, well-paid Google engineer when Microsoft made him an offer that 10x’d his salary (!!). When Kelsey told Google he was planning to take the offer, it matched the offer, proving that his market value had massively increased. It shows that being well paid doesn’t necessarily mean you’re being paid at the correct market rate.

Gergely Orosz

61,328 次观看 • 2 个月前

🚨Here's how the Chinese regime perfected a system that allowed the CCP to survive where the Soviet Union failed. China scholar Chenggang Xu calls it regionally administered totalitarianism (RADT). Here's how it works: The CCP gives broad commands like "eliminate Falun Gong" or "expand organ transplants." Local officials then compete viciously to prove their loyalty by achieving these goals in the most "effective" ways possible. Think of it as a twisted performance review system. Officials get promoted based on ruthlessness, not morality. The result? A self-propelling machine of horror that radicalizes itself. Imagine this: Bo Xilai was a senior official in northeastern China. When the CCP started locking up millions of Falun Gong practitioners in 1999, he saw an opportunity. His ally, police chief Wang Lijun ran experiments on lethal injection methods that preserved organs for transplantation. At some point, someone came up with a horrific idea: They could use detained practitioners as involuntary organ sources - solving a political problem while building an extremely profitable transplant industry. Wang later bragged publicly about participating in thousands of transplant cases involving executed prisoners. This wasn't a secret operation - it was career advancement. Bo also saw his career flourish. The system then scaled horrifically. Mass incarceration, medical profiling of detainees, privileged "red card" holders getting rapid transplants, and military logistics moving matched victims to hospitals for kill-to-order procedures. Other regions copied this model, each trying to outdo the others in demonstrating loyalty through brutality. I wrote "Killed to Order" to connect these dots. It's the book the CCP doesn't want you to read (in fact we've already seen two fake versions of the book spring up on Amazon). That alone should tell you something.

🚨Here's how the Chinese regime perfected a system that allowed the CCP to survive where the Soviet Union failed. China scholar Chenggang Xu calls it regionally administered totalitarianism (RADT). Here's how it works: The CCP gives broad commands like "eliminate Falun Gong" or "expand organ transplants." Local officials then compete viciously to prove their loyalty by achieving these goals in the most "effective" ways possible. Think of it as a twisted performance review system. Officials get promoted based on ruthlessness, not morality. The result? A self-propelling machine of horror that radicalizes itself. Imagine this: Bo Xilai was a senior official in northeastern China. When the CCP started locking up millions of Falun Gong practitioners in 1999, he saw an opportunity. His ally, police chief Wang Lijun ran experiments on lethal injection methods that preserved organs for transplantation. At some point, someone came up with a horrific idea: They could use detained practitioners as involuntary organ sources - solving a political problem while building an extremely profitable transplant industry. Wang later bragged publicly about participating in thousands of transplant cases involving executed prisoners. This wasn't a secret operation - it was career advancement. Bo also saw his career flourish. The system then scaled horrifically. Mass incarceration, medical profiling of detainees, privileged "red card" holders getting rapid transplants, and military logistics moving matched victims to hospitals for kill-to-order procedures. Other regions copied this model, each trying to outdo the others in demonstrating loyalty through brutality. I wrote "Killed to Order" to connect these dots. It's the book the CCP doesn't want you to read (in fact we've already seen two fake versions of the book spring up on Amazon). That alone should tell you something.

Jan Jekielek

537,676 次观看 • 4 个月前

Episode #103: PhD to $100M Revenue: Rebuilding SMB Lending with AI Sahill Poddar (Sahill Poddar) and his team have quietly built Parafin to nearly $100m GAAP ARR in only four years, in an industry that's become a Silicon Valley graveyard: SMB lending. Sahill talks about how they partnered with other marketplaces, vertical SaaS, and point of sale providers to offer financial services to SMBs at scale, landing DoorDash as their first customer before building the product, and advice for technical teams learning enterprise sales. Sahill's a fascinating founder, as he started his career getting a PhD discovering the Higgs boson particle at CERN’s Large Hadron Collider. We talk about physics, he explains how the Large Hadron Collider works, why physics is just real world machine learning, and all the lessons he learned on the growth teams at Facebook and Robinhood (including the way Robinhood acquired most of its userbase!) Thanks to Ramp for supporting this episode, and to Hans Tung (Notable Capital), Nick Shalek (Ribbit Capital), and Mahdi Raza (Pathlight Ventures) for their help brainstorming topics for Sahill. Stream the full episode here or grab a link below. Timestamps: 4:06 Lending to SMBs inside marketplaces and platforms 9:39 Why SMB lending is so hard 12:50 Three ways AI is changing Fintech 16:47 Silicon Valley’s graveyard of SMB lenders 22:44 Getting a PhD in Particle Physics 26:15 How CERN's Large Hadron Collider works 31:49 Discovering new dimensions 34:10 Building billion user data sets at Facebook 39:53 Working with other physicists at Robinhood 50:29 Growth lessons from FB + Robinhood 1:00:57 Starting Parafin, embedded, horizontal SMB lending 1:06:09 Why credit is the biggest problem for SMBs 1:10:53 Raising a Seed from Ribbit pre-product 1:13:25 Landing DoorDash as the first customer 1:16:51 Mastering B2B sales as a technical founder 1:22:58 Lessons from Vlad at Robinhood

Episode #103: PhD to $100M Revenue: Rebuilding SMB Lending with AI Sahill Poddar (Sahill Poddar) and his team have quietly built Parafin to nearly $100m GAAP ARR in only four years, in an industry that's become a Silicon Valley graveyard: SMB lending. Sahill talks about how they partnered with other marketplaces, vertical SaaS, and point of sale providers to offer financial services to SMBs at scale, landing DoorDash as their first customer before building the product, and advice for technical teams learning enterprise sales. Sahill's a fascinating founder, as he started his career getting a PhD discovering the Higgs boson particle at CERN’s Large Hadron Collider. We talk about physics, he explains how the Large Hadron Collider works, why physics is just real world machine learning, and all the lessons he learned on the growth teams at Facebook and Robinhood (including the way Robinhood acquired most of its userbase!) Thanks to Ramp for supporting this episode, and to Hans Tung (Notable Capital), Nick Shalek (Ribbit Capital), and Mahdi Raza (Pathlight Ventures) for their help brainstorming topics for Sahill. Stream the full episode here or grab a link below. Timestamps: 4:06 Lending to SMBs inside marketplaces and platforms 9:39 Why SMB lending is so hard 12:50 Three ways AI is changing Fintech 16:47 Silicon Valley’s graveyard of SMB lenders 22:44 Getting a PhD in Particle Physics 26:15 How CERN's Large Hadron Collider works 31:49 Discovering new dimensions 34:10 Building billion user data sets at Facebook 39:53 Working with other physicists at Robinhood 50:29 Growth lessons from FB + Robinhood 1:00:57 Starting Parafin, embedded, horizontal SMB lending 1:06:09 Why credit is the biggest problem for SMBs 1:10:53 Raising a Seed from Ribbit pre-product 1:13:25 Landing DoorDash as the first customer 1:16:51 Mastering B2B sales as a technical founder 1:22:58 Lessons from Vlad at Robinhood

The Peel

23,493 次观看 • 1 年前

A PhD student built a working nuclear fusion reactor in his garage, let an AI run it, and 400 thousand dollars later he works for Elon Musk. he posted it once. that single post ended with a grant in his account and a job offer from the most powerful man on earth. not a simulation. not a school project. an actual device that fuses atoms, sitting where his car used to be. fusion is the thing governments have been chasing for 70 years with billion dollar labs. the hard part was never the reactor itself. it was the control. the plasma inside has to be held at conditions hotter than the core of the sun, and it shifts and collapses in milliseconds. no human can react fast enough to keep it stable. so he stopped trying to do it himself. he handed the control loop to an AI. the model reads the sensor data hundreds of times a second, predicts how the plasma is about to move, and adjusts the magnetic fields before it ever drifts out of line. it does not wait for the plasma to misbehave. it sees it coming and corrects it before it happens. the same reaction-before-the-event speed no person could ever match. this is the exact kind of build people are tearing apart inside Neuro Club. not to make reactors, but because the workflow is identical for anything hard. let the AI run the loop, predict the problem, fix it before it breaks. same playbook whether it is plasma or a business. then the post went out. within days Elon's fusion team reached out. they did not ask him to interview for an entry role. they handed him a 400 thousand dollar grant and pulled him onto the team building this at scale. one garage build turned a PhD student into an operator for the most ambitious man alive. here is the part that should stop you. he was one guy with a PhD, a garage, and an AI model doing the job that entire teams of physicists used to fail at. the AI was not assisting him. it was the operator. he built the hardware. the machine ran it. and that was enough to get noticed at the very top. most people think AI writes emails and makes pictures. meanwhile someone pointed it at one of the hardest physics problems on earth, held the plasma steady, and got paid by Elon Musk for it. the gap is not between humans and AI anymore. it is between the people who realize what this thing can already do and the people still using it to summarize their inbox.

A PhD student built a working nuclear fusion reactor in his garage, let an AI run it, and 400 thousand dollars later he works for Elon Musk. he posted it once. that single post ended with a grant in his account and a job offer from the most powerful man on earth. not a simulation. not a school project. an actual device that fuses atoms, sitting where his car used to be. fusion is the thing governments have been chasing for 70 years with billion dollar labs. the hard part was never the reactor itself. it was the control. the plasma inside has to be held at conditions hotter than the core of the sun, and it shifts and collapses in milliseconds. no human can react fast enough to keep it stable. so he stopped trying to do it himself. he handed the control loop to an AI. the model reads the sensor data hundreds of times a second, predicts how the plasma is about to move, and adjusts the magnetic fields before it ever drifts out of line. it does not wait for the plasma to misbehave. it sees it coming and corrects it before it happens. the same reaction-before-the-event speed no person could ever match. this is the exact kind of build people are tearing apart inside Neuro Club. not to make reactors, but because the workflow is identical for anything hard. let the AI run the loop, predict the problem, fix it before it breaks. same playbook whether it is plasma or a business. then the post went out. within days Elon's fusion team reached out. they did not ask him to interview for an entry role. they handed him a 400 thousand dollar grant and pulled him onto the team building this at scale. one garage build turned a PhD student into an operator for the most ambitious man alive. here is the part that should stop you. he was one guy with a PhD, a garage, and an AI model doing the job that entire teams of physicists used to fail at. the AI was not assisting him. it was the operator. he built the hardware. the machine ran it. and that was enough to get noticed at the very top. most people think AI writes emails and makes pictures. meanwhile someone pointed it at one of the hardest physics problems on earth, held the plasma steady, and got paid by Elon Musk for it. the gap is not between humans and AI anymore. it is between the people who realize what this thing can already do and the people still using it to summarize their inbox.

Born to gamble

347,287 次观看 • 2 个月前

SpaceX is going public on Nasdaq today under ticker SPCX and Elon Musk just showed you exactly what he is building (Save this). Elon Musk is standing inside a Gigabay being built to produce 1,000 Starships per year. He says a second facility is going up in Florida, so two factories, each designed at a scale that by some measures will make them the largest structures ever built. And then he said that the Starlink V3 satellites, each the size of a 737, manufactured at a rate of 5,000 to 10,000 per year. Tesla at its peak is still building far more complex manufactured tonnage than SpaceX which is his way of saying that 1,000 Starships a year is not science fiction, but rather an engineering problem that humans have already solved in other industries. That framing is the entire bull case. SpaceX priced at $135 per share, raising $75 billion at a $1.77 trillion valuation, the largest IPO ever recorded. The deal was well oversubscribed before the roadshow even closed. But the number that matters is not the valuation, it is the production target. SpaceX's prospectus identifies $28.5 trillion in total addressable markets across broadband, AI infrastructure, and space. When Musk says he is building Starships at the scale of commercial aviation, he is telling you the infrastructure layer underneath the entire AI economy is about to get a new backbone. The market will price that in starting today. Come join Milk Road Pro for our full breakdown, how to think about SPCX as part of an AI infrastructure portfolio, and out entire AI thesis. Link below!

SpaceX is going public on Nasdaq today under ticker SPCX and Elon Musk just showed you exactly what he is building (Save this). Elon Musk is standing inside a Gigabay being built to produce 1,000 Starships per year. He says a second facility is going up in Florida, so two factories, each designed at a scale that by some measures will make them the largest structures ever built. And then he said that the Starlink V3 satellites, each the size of a 737, manufactured at a rate of 5,000 to 10,000 per year. Tesla at its peak is still building far more complex manufactured tonnage than SpaceX which is his way of saying that 1,000 Starships a year is not science fiction, but rather an engineering problem that humans have already solved in other industries. That framing is the entire bull case. SpaceX priced at $135 per share, raising $75 billion at a $1.77 trillion valuation, the largest IPO ever recorded. The deal was well oversubscribed before the roadshow even closed. But the number that matters is not the valuation, it is the production target. SpaceX's prospectus identifies $28.5 trillion in total addressable markets across broadband, AI infrastructure, and space. When Musk says he is building Starships at the scale of commercial aviation, he is telling you the infrastructure layer underneath the entire AI economy is about to get a new backbone. The market will price that in starting today. Come join Milk Road Pro for our full breakdown, how to think about SPCX as part of an AI infrastructure portfolio, and out entire AI thesis. Link below!

Milk Road AI

48,404 次观看 • 2 个月前

When 80 Minutes Wasn't Enough to Save a Life - This is so heartbreaking 💔 Yuvraj Mehta, 27, stood on his sinking car for nearly 80 minutes, screaming "Papa, save me. I don't want to die." His father heard him. Police saw him. Fire brigade arrived. SDRF came. NDRF joined. Everyone watched. Nobody entered the water. The reasons? Fog. Cold water. Iron rods below. Too dangerous, they said. A delivery boy tried saving him with a rope. He failed, but at least he tried. This unknown hero showed more courage than all the trained rescue teams combined. His car Grand Vitara had fallen into a 70-foot drain through a broken wall in Noida Sector 150 at around 12:20 AM. At 1:45 am, Yuvraj's screams stopped. He drowned just meters away from his helpless father and dozens of officials. Locals had complained about this dangerous spot many times before. The Noida Authority ignored them. Only after Yuvraj died did they finally dump debris there. This young techie was his family's only earning member after losing his mother two years ago. His retired father now sits alone, haunted by his son's last words. This is the India we live in... where help arrives but rescue doesn't. Where a delivery boy has more heart than entire rescue teams. Where we fix problems only after someone dies. Yuvraj deserved better. Every citizen deserves better. Yuvraj was from Noida and worked as a software engineer in Gurugram.

When 80 Minutes Wasn't Enough to Save a Life - This is so heartbreaking 💔 Yuvraj Mehta, 27, stood on his sinking car for nearly 80 minutes, screaming "Papa, save me. I don't want to die." His father heard him. Police saw him. Fire brigade arrived. SDRF came. NDRF joined. Everyone watched. Nobody entered the water. The reasons? Fog. Cold water. Iron rods below. Too dangerous, they said. A delivery boy tried saving him with a rope. He failed, but at least he tried. This unknown hero showed more courage than all the trained rescue teams combined. His car Grand Vitara had fallen into a 70-foot drain through a broken wall in Noida Sector 150 at around 12:20 AM. At 1:45 am, Yuvraj's screams stopped. He drowned just meters away from his helpless father and dozens of officials. Locals had complained about this dangerous spot many times before. The Noida Authority ignored them. Only after Yuvraj died did they finally dump debris there. This young techie was his family's only earning member after losing his mother two years ago. His retired father now sits alone, haunted by his son's last words. This is the India we live in... where help arrives but rescue doesn't. Where a delivery boy has more heart than entire rescue teams. Where we fix problems only after someone dies. Yuvraj deserved better. Every citizen deserves better. Yuvraj was from Noida and worked as a software engineer in Gurugram.

زماں

543,510 次观看 • 6 个月前

Michael Saylor spent 2025 telling people to sell a kidney before selling Bitcoin. Then he literally dumped $323 million of it since May. He just went on Diary of a CEO and exposed himself as a hypocrite in his own words: His company holds 842,138 Bitcoin. That is roughly 4% of every coin that will ever exist, which makes it the largest corporate holder on Earth. In February 2025 he posted "Sell a kidney if you must, but keep the Bitcoin." Weeks earlier he had written that selling weakens the network. But here is what his own filings show since May: - 32 Bitcoin sold between May 26 and May 31 for $2.5 million - 3,588 Bitcoin sold between June 29 and July 5 for $216 million, the largest disposal in company history - 1,638 Bitcoin sold between July 27 and August 2 for $104.73 million That last batch went out at an average of $63,957 a coin. His average purchase price is around $75,400. He sold at a LOSS. New Bitcoin purchases are currently paused. So why is the loudest Bitcoin bull alive selling coins for less than he paid? The answer is a bill he built himself, and he walked through the whole thing on that podcast without once connecting it to the selling. He went to ChatGPT and asked it to design a security nobody had ever built, a preferred stock where he could change the dividend rate every month. He says the lawyers and bankers told him it had never been done before. He brought it to market as a $2.5 billion IPO, then sold another $8 billion off a shelf registration. His own framing of that: Selling $15 billion of credit "kind of equates to the company making about $15 billion." Selling credit is borrowing. Every dollar came attached to a dividend he now has to pay in cash, forever, whether Bitcoin goes up or not. Strategy owes over $1.7 billion a year across five preferred instruments and its debt. The software business does not come close to covering that. So the Bitcoin covers it. The break-even is 3.2%. So if Bitcoin appreciates 3.2% a year, they can pay those dividends indefinitely by selling Bitcoin to do it. He called the sale a one-time demonstration to break a short seller narrative and said selling is not the primary strategy. His own CEO Phong Le told the Q2 earnings call that Strategy will sell whenever management finds it advantageous, and that investors should expect more going forward. Now look at who ends up holding the bag: That preferred stock is majority owned by retail investors, and it dropped to $89 against a $100 par value in June. Retail Bitcoin holders get told to hold through anything while retail preferred holders get paid a yield funded by selling that same Bitcoin at a loss. He opened that same episode explaining that Bitcoin lets you own something nobody more powerful can take away from you. In 2024 he paid $40 million to settle what the DC Attorney General called the LARGEST income tax fraud recovery in the city's history. The complaint said he claimed residency in Florida while his own security logs placed him in Washington for 1,397 days against 449 in Florida. He settled without admitting wrongdoing and still disputes living there. In 2000 he settled SEC accounting fraud charges after his company reported profits during years it was actually losing money. He disgorged $8.28 million and admitted nothing. Strategy lost $12.54 billion in the first quarter and another $8.22 billion in the second. And this week he sat there and told a 25 year old with a few hundred dollars to buy Bitcoin and hold it for a decade. But he is not doing that himself. His company just SOLD $105 million of it. What do you think of Saylor?

Michael Saylor spent 2025 telling people to sell a kidney before selling Bitcoin. Then he literally dumped $323 million of it since May. He just went on Diary of a CEO and exposed himself as a hypocrite in his own words: His company holds 842,138 Bitcoin. That is roughly 4% of every coin that will ever exist, which makes it the largest corporate holder on Earth. In February 2025 he posted "Sell a kidney if you must, but keep the Bitcoin." Weeks earlier he had written that selling weakens the network. But here is what his own filings show since May: - 32 Bitcoin sold between May 26 and May 31 for $2.5 million - 3,588 Bitcoin sold between June 29 and July 5 for $216 million, the largest disposal in company history - 1,638 Bitcoin sold between July 27 and August 2 for $104.73 million That last batch went out at an average of $63,957 a coin. His average purchase price is around $75,400. He sold at a LOSS. New Bitcoin purchases are currently paused. So why is the loudest Bitcoin bull alive selling coins for less than he paid? The answer is a bill he built himself, and he walked through the whole thing on that podcast without once connecting it to the selling. He went to ChatGPT and asked it to design a security nobody had ever built, a preferred stock where he could change the dividend rate every month. He says the lawyers and bankers told him it had never been done before. He brought it to market as a $2.5 billion IPO, then sold another $8 billion off a shelf registration. His own framing of that: Selling $15 billion of credit "kind of equates to the company making about $15 billion." Selling credit is borrowing. Every dollar came attached to a dividend he now has to pay in cash, forever, whether Bitcoin goes up or not. Strategy owes over $1.7 billion a year across five preferred instruments and its debt. The software business does not come close to covering that. So the Bitcoin covers it. The break-even is 3.2%. So if Bitcoin appreciates 3.2% a year, they can pay those dividends indefinitely by selling Bitcoin to do it. He called the sale a one-time demonstration to break a short seller narrative and said selling is not the primary strategy. His own CEO Phong Le told the Q2 earnings call that Strategy will sell whenever management finds it advantageous, and that investors should expect more going forward. Now look at who ends up holding the bag: That preferred stock is majority owned by retail investors, and it dropped to $89 against a $100 par value in June. Retail Bitcoin holders get told to hold through anything while retail preferred holders get paid a yield funded by selling that same Bitcoin at a loss. He opened that same episode explaining that Bitcoin lets you own something nobody more powerful can take away from you. In 2024 he paid $40 million to settle what the DC Attorney General called the LARGEST income tax fraud recovery in the city's history. The complaint said he claimed residency in Florida while his own security logs placed him in Washington for 1,397 days against 449 in Florida. He settled without admitting wrongdoing and still disputes living there. In 2000 he settled SEC accounting fraud charges after his company reported profits during years it was actually losing money. He disgorged $8.28 million and admitted nothing. Strategy lost $12.54 billion in the first quarter and another $8.22 billion in the second. And this week he sat there and told a 25 year old with a few hundred dollars to buy Bitcoin and hold it for a decade. But he is not doing that himself. His company just SOLD $105 million of it. What do you think of Saylor?

Ricardo

19,901 次观看 • 6 天前

Only one driver in F1 history has been so fast he almost bankrupted his own team. His name is Kimi Raikkonen. 2012: Lotus signed him on €8M base salary, plus a "safe" bonus of €50,000 per championship point. They assumed the car wasn't fast enough to worry about. 390 points later, 15 podiums, 2 wins. That triggered €19.5M in bonuses. Three times his base salary. By the end of 2013, Kimi told the BBC: "I have not been paid a single euro all year." €17M owed. He threatened to boycott the final two races and missed them anyway because of back surgery. According to his biographer, he was still owed €5M years later but he never sued. He said he didn't want anyone at the team to lose their job. Lotus signed a former World Champion expecting a comeback story. What they got was one of the greatest value for money seasons in F1 history and a bonus bill they couldn't afford to pay. Best clause in F1 history or worst business decision ever?

Only one driver in F1 history has been so fast he almost bankrupted his own team. His name is Kimi Raikkonen. 2012: Lotus signed him on €8M base salary, plus a "safe" bonus of €50,000 per championship point. They assumed the car wasn't fast enough to worry about. 390 points later, 15 podiums, 2 wins. That triggered €19.5M in bonuses. Three times his base salary. By the end of 2013, Kimi told the BBC: "I have not been paid a single euro all year." €17M owed. He threatened to boycott the final two races and missed them anyway because of back surgery. According to his biographer, he was still owed €5M years later but he never sued. He said he didn't want anyone at the team to lose their job. Lotus signed a former World Champion expecting a comeback story. What they got was one of the greatest value for money seasons in F1 history and a bonus bill they couldn't afford to pay. Best clause in F1 history or worst business decision ever?

BILLIONAIRE UNIVERSITY®

271,458 次观看 • 2 个月前