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BREAKING: Inside AppLovin's $100B+ Ad Engine Co-Founder & CEO Adam Foroughi CTO Giovanni Ge FULL INTERVIEW AppLovin is one of the most efficient companies in public markets at ~400 people. Approaching $7B in EBITDA. That's ~$17M per employee, roughly 3x the revenue per head of Nvidia & 7x Apple,...

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Molly O’Shea

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698,599 次观看 • 1 个月前 •via X (Twitter)

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36 条评论

amit 的头像
amit1 个月前

Very curious to learn about Applovin. Timely interview. Thank you!!

Molly O’Shea 的头像
Molly O’Shea1 个月前

Adam + Gio = Alpha

David Senra 的头像
David Senra1 个月前

Adam is the best founders no one’s ever heard of. That story of him borrowing $6 to buy back his stock is nuts.

Molly O’Shea 的头像
Molly O’Shea1 个月前

Have you met him?

Rob Mohr 的头像
Rob Mohr1 个月前

@davidsenra "May I meet you?"

Molly O’Shea 的头像
Molly O’Shea1 个月前

@davidsenra 😂😂😂😭

AppLovin 的头像
AppLovin1 个月前

Thanks for having Adam and Gio on the pod!

Molly O’Shea 的头像
Molly O’Shea1 个月前

Thanks for hosting us at your HQ!!

Rafael Vivas 的头像
Rafael Vivas1 个月前

Definitely going to apply for a job at @AppLovin.

Molly O’Shea 的头像
Molly O’Shea1 个月前

@AppLovin Didn’t you try to hire Adam when you were 16?

Rafael Vivas 的头像
Rafael Vivas1 个月前

@AppLovin Yeah. He then proceeded to remind me that he owned half of that company anyway, and that I don't know shit about building big businesses.

Molly O’Shea 的头像
Molly O’Shea1 个月前

@AppLovin

Molly O’Shea 的头像
Molly O’Shea1 个月前

Listen on Spotify: Apple: Watch on YT:

Giovanni Ge 的头像
Giovanni Ge1 个月前

It was a great conversation, thanks for having us

HP Claire 的头像
HP Claire1 个月前

Now that's a successful journey. 400 employees 7B in EBITDA 👀

daniel garcia 的头像
daniel garcia1 个月前

insane team

Ty 的头像
Ty1 个月前

This is a great interview! Molly you asked some great questions!

Molly O’Shea 的头像
Molly O’Shea1 个月前

thank you! they're incredible

Wealthmatica 的头像
Wealthmatica1 个月前

@AppLovin Great interview!

Rizal The Professor 的头像
Rizal The Professor1 个月前

@grok RZ = Neanderthal maternal/mtDNA chronological series ЯZ = Neanderthal paternal/Y-DNA chronological series RZL = Neanderthal × Denisovan hybrid category

Rachael Nelson 的头像
Rachael Nelson1 个月前

Great editing on this.

ArthurKaInvest 的头像
ArthurKaInvest1 个月前

Watching this interview right now about AppLovin thanks! $APP

TheBahtMover 的头像
TheBahtMover1 个月前

Thank you for the interview 👍

Felix 的头像
Felix26 天前

Good video!

Michael 的头像
Michael1 个月前

Great Interview! Applovin story is truly amazing

Molly O’Shea 的头像
Molly O’Shea1 个月前

🔥🔥🔥

𝖊𝖑𝖋𝖞 的头像
𝖊𝖑𝖋𝖞1 个月前

Adam Foroughi and Giovanni Ge really nailed execution at AppLovin.

PodHood 的头像
PodHood1 个月前

Transcripts, Highlights & MCP for this episode @MollySOShea @xc_giovanni_ge @AppLovin

Michael Perdomo 的头像
Michael Perdomo1 个月前

400 people, 17M EBITDA per head. the ad engine scales, not headcount. real question is how much of that margin holds when ecommerce outside gaming kicks in.

SurgicalCaps.com 的头像
SurgicalCaps.com1 个月前

doesn't see it? AppLovin's $100B+ Ad Engine

YouTube Conversation 的头像
YouTube Conversation1 个月前

The tension worth exploring: AppLovin kept engineering flat while cash flow scaled dramatically. Paste the full interview into ChatGPT with YouTube Conversation and ask: what operating choices do Adam and Gio credit most?

Tony Tong | Founder | Ancient Systems x AI 的头像
Tony Tong | Founder | Ancient Systems x AI1 个月前

Efficiency at that scale is never one clean system, it's an ensemble of unglamorous parts in sync. Our Face AI runs the same way: EOS3, EOS18, a facial action unit model, and a KNN model combined into one output. Nobody brags about the plumbing, but it makes the number real.

the editor company 的头像
the editor company1 个月前

very inspiring. i wanna be a billion dollar company too now.

bonzo 的头像
bonzo1 个月前

these timestamps are gold! boutta watch it on my flight tm morning. ty for posting 🫶

Unchained soul 的头像
Unchained soul1 个月前

great interview

Ron Deckard 的头像
Ron Deckard1 个月前

Awesome interview

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AppLovin CEO is trying to counter Anthropic’s CEO claims that SaaS is DEAD and never coming back. $APP is not an ad network. It is not a gaming company. It is an arbitrage engine with 400 engineers, $1.3 billion in cash flow per quarter, and a capital allocation track record that belongs in a business school curriculum. Start with the gaming studios. From 2018 to 2023, AppLovin acquired over 15 mobile gaming studios and 1,500 people. Nobody understood why. The answer was data. Third party advertisers would not share their conversion and ROAS data with AppLovin's model. So AppLovin became a first party advertiser — running its own games, generating its own behavioral data, feeding that data into Axon, its deep learning ad model. The studios were not a business. They were a training set. The moment Axon 2.0 launched in April 2023 and proved so effective that the entire gaming industry had to plug in and share their data to access the returns, the studios had served their purpose. AppLovin sold the entire portfolio to Tripledot and moved on. They used the asset to build the moat and immediately dumped the asset. That is not a pivot. That is a premeditated extraction. The 2022 buyback is the capital allocation move that defined the company's trajectory. The market had pushed AppLovin's market cap to $3.8 billion. The business was printing over $1 billion in EBITDA. Instead of open market repurchases, management identified that private equity backers and early insiders controlled nearly 50% of the float and needed liquidity. They bypassed the public market entirely, negotiated directly with those institutional holders, and executed a $6 billion leveraged buyback at the floor. Foroughi estimates that single decision generated $50 to $60 billion in retained value for remaining shareholders as the stock recovered. One negotiation. One decision. $50 billion created. The operating model is the part that should make every other tech CEO uncomfortable. AppLovin fired 40% of its workforce while growing revenue nearly 100% year over year. The C suite is four people—CEO, CFO, CTO, General Counsel. No CRO. No COO. No salesforce. Over 80% of the codebase is now written by AI, multiplying the output of their best engineers by up to 100x. The product does not need to be sold. Advertisers plug in, set a performance goal, and if the ROAS is positive they scale spend infinitely. The platform turns advertisers into blind arbitrageurs — they do not need to understand how it works, they just need to see the return. The TAM expansion is the next leg. Axon perfected gaming monetization. It is now being pointed at ecommerce and local SMBs — markets orders of magnitude larger than mobile gaming. The model does not need a sales team to penetrate them. It needs inventory and intent signals. It is acquiring both. The company has internal compensation triggers tied to a $1 trillion market cap. They went from $3.8 billion in 2022 to $154 billion today. The people running this business have done everything they said they would do, faster than anyone expected, with fewer people than anyone thought possible. That track record is the most important input in any forward model. Betting against a team that turned a $3.8 billion floor into $154 billion in three years, with 400 people, no salesforce, and an AI model that the entire industry has to use — requires a very specific and defensible thesis. Most of the people making that bet do not have one…stock is still being beaten to death…still not compelled. AI companies are just too good and getting better but interesting to see him come publicly to try to pump his stock

Nicholas Mugalli

100,854 次观看 • 4 个月前

The recent 20VC interview with Adam Foroughi is an incredible example of capital allocation and lean operations. $APP is operating in a league of its own right now. We are talking about a business that generated $5.48B in 2025 revenue and is currently doing roughly $10 M in EBITDA per employee. They operate with a total headcount of around 895 people, but the core advertising unit that prints the cash is only about ~400 people. You never see software companies at a $150 B market cap running this lean. The 84% EBITDA margin profile is completely disconnected from traditional enterprise SaaS norms. The competitive moat is their recommendation engine, AXON 2.0. The advertising environment is brutal. $META and TikTok have elite targeting engines. TikTok’s algorithm is famous for engagement without a social graph. But AppLovin has built an engine that processes over 2M ad auctions per second, optimizing heavily for performance and incrementality. When the stock completely collapsed by 92% in 2022 management did not panic. Instead, they ruthlessly threw out their old ML infrastructure and rebuilt AXON from the ground up. They ignored the noise, shut down investor relations temporarily to focus internally, and executed one of the highest ROI buybacks in modern corporate history. They targeted private market VC sellers who were desperate for liquidity. That specific buyback maneuver alone accounts for roughly $50 BILLION of the company's current market value. Their internal culture is intensely anti-bloat. They have zero traditional management layers. There is no CRO, no COO, no CMO, and no Chief Human Resources Officer. Foroughi cut HR from roughly 80 people down to 15. The product team does not exist. Engineers are required to act as product managers. If an engineer cannot understand the business KPIs that drive revenue, they do not belong there. AI enables them to eliminate process-heavy roles. They demand that only their top 10% to 15% of employees take equity risk, while everyone else gets cash compensation. This keeps their SBC tightly capped at around $300 M annually. For a company valued at over $150 B, that dilution rate is almost a rounding error. Foroughi values the business strictly on cash flow minus SBC. It is the absolute cleanest metric. The bearish narrative assumes AI makes game and app creation easier, which somehow dilutes the value of AppLovin's ecosystem. Foroughi counters that cheaper content creation actually explodes the volume of content, making discovery engines like AXON infinitely more valuable. If they expand this performance-based advertising model into connected TV and the broader $170 B e-commerce sector via their Axon Ads Manager, the path to a trillion-dollar valuation becomes very achievable without even needing to build a native social network. Great interview! Really appreciate hearing Adam.
2:06

The recent 20VC interview with Adam Foroughi is an incredible example of capital allocation and lean operations. $APP is operating in a league of its own right now. We are talking about a business that generated $5.48B in 2025 revenue and is currently doing roughly $10 M in EBITDA per employee. They operate with a total headcount of around 895 people, but the core advertising unit that prints the cash is only about ~400 people. You never see software companies at a $150 B market cap running this lean. The 84% EBITDA margin profile is completely disconnected from traditional enterprise SaaS norms. The competitive moat is their recommendation engine, AXON 2.0. The advertising environment is brutal. $META and TikTok have elite targeting engines. TikTok’s algorithm is famous for engagement without a social graph. But AppLovin has built an engine that processes over 2M ad auctions per second, optimizing heavily for performance and incrementality. When the stock completely collapsed by 92% in 2022 management did not panic. Instead, they ruthlessly threw out their old ML infrastructure and rebuilt AXON from the ground up. They ignored the noise, shut down investor relations temporarily to focus internally, and executed one of the highest ROI buybacks in modern corporate history. They targeted private market VC sellers who were desperate for liquidity. That specific buyback maneuver alone accounts for roughly $50 BILLION of the company's current market value. Their internal culture is intensely anti-bloat. They have zero traditional management layers. There is no CRO, no COO, no CMO, and no Chief Human Resources Officer. Foroughi cut HR from roughly 80 people down to 15. The product team does not exist. Engineers are required to act as product managers. If an engineer cannot understand the business KPIs that drive revenue, they do not belong there. AI enables them to eliminate process-heavy roles. They demand that only their top 10% to 15% of employees take equity risk, while everyone else gets cash compensation. This keeps their SBC tightly capped at around $300 M annually. For a company valued at over $150 B, that dilution rate is almost a rounding error. Foroughi values the business strictly on cash flow minus SBC. It is the absolute cleanest metric. The bearish narrative assumes AI makes game and app creation easier, which somehow dilutes the value of AppLovin's ecosystem. Foroughi counters that cheaper content creation actually explodes the volume of content, making discovery engines like AXON infinitely more valuable. If they expand this performance-based advertising model into connected TV and the broader $170 B e-commerce sector via their Axon Ads Manager, the path to a trillion-dollar valuation becomes very achievable without even needing to build a native social network. Great interview! Really appreciate hearing Adam.

CapexAndChill

10,754 次观看 • 5 个月前

Adam Foroughi was just asked how AppLovin becomes a $1T company. Here’s the clear roadmap he laid out: We’re already at a ~$7B+ EBITDA run-rate and convert ~75% of that into cash after SBC. To be worth a trillion dollars, we need to get to $30B+ of annual cash flow. That’s the number that supports the multiple. How we get there: 1. Mobile gaming is not big enough: We’ve already built the #1 platform for mobile game developers. That category alone cannot generate the cash flow required. 2. Expand into broader consumer advertising: This dramatically widens the addressable dollars and the types of companies we can serve. It’s the near-to-medium term (5+ years) growth engine. 3. Then expand into adjacent categories: Every company that went from a strong core product to a trillion-dollar valuation did the same thing: generate massive value in one area, then execute exceptionally well in adjacent ones. That’s the longer-term work. The business has almost no capex. As we scale, the data advantage, network effects, and technology only get stronger — which is why the bigger goals, while daunting, actually become more achievable over time.We’ve gone $1B → $10B → $100B market cap by setting the next zero and executing. The same playbook continues. $APP ___ 🎙️ Sourcery with Molly O’Shea | Aug 14 2026
4:01

Adam Foroughi was just asked how AppLovin becomes a $1T company. Here’s the clear roadmap he laid out: We’re already at a ~$7B+ EBITDA run-rate and convert ~75% of that into cash after SBC. To be worth a trillion dollars, we need to get to $30B+ of annual cash flow. That’s the number that supports the multiple. How we get there: 1. Mobile gaming is not big enough: We’ve already built the #1 platform for mobile game developers. That category alone cannot generate the cash flow required. 2. Expand into broader consumer advertising: This dramatically widens the addressable dollars and the types of companies we can serve. It’s the near-to-medium term (5+ years) growth engine. 3. Then expand into adjacent categories: Every company that went from a strong core product to a trillion-dollar valuation did the same thing: generate massive value in one area, then execute exceptionally well in adjacent ones. That’s the longer-term work. The business has almost no capex. As we scale, the data advantage, network effects, and technology only get stronger — which is why the bigger goals, while daunting, actually become more achievable over time.We’ve gone $1B → $10B → $100B market cap by setting the next zero and executing. The same playbook continues. $APP ___ 🎙️ Sourcery with Molly O’Shea | Aug 14 2026

Dimitry Nakhla | Babylon Capital®

19,616 次观看 • 1 个月前

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59:39

Today, more than 3% of the world's lawyers use Legora, and the company has grown from $1 million to $100 million in ARR since launching in October 2024. At Startup School 2026, Legora co-founder and CEO Max Junestrand shares how they built one of the fastest-growing enterprise software companies in the world, from cold emailing lawyers and moving into a customer's office to freezing sales for six months to rebuild the product. He explains why building a company is ultimately about people, how to create a culture that wants to win, and why founders have to learn to love the hustle. 00:07 — From 3 Engineers to $100M ARR 03:15 — How Legora Got Started 05:09 — Learning the Legal Industry From Scratch 07:22 — Getting Rejected by YC 09:36 — Moving Into a Customer's Office 11:11 — Going From Zero to $1M ARR During YC 13:26 — Why We Froze Sales for Six Months 15:18 — Rebuilding the Product From Scratch 17:26 — Building a Company Is About People 21:51 — Creating a Culture That Wants to Win 25:26 — You Have to Love the Hustle 27:55 — Do Founders Need Domain Expertise? 29:14 — Betting on Models Getting Better 36:29 — When Should You Start a Company? 45:04 — How to Become More Ambitious 48:18 — The Skills Founders Need Today

Y Combinator

78,443 次观看 • 1 个月前

There is a massive misunderstanding about what $APP actually is, and the recent Adam Foroughi interview just handed investors as a class in capital allocation, efficiency, and strategic vertical integration. At its core, AppLovin isn’t solely an ad-network or a gaming business but an arbitrage engine. APP is essentially run by a skeleton crew of about ~400 core engineers and product builders generating $1.3B in cash flow a quarter. The gaming studio acquisitions were purely strategic. From roughly 2018 to 2023, APP bought up mobile gaming studios. It was accumulating over 15 studios and 1,500 headcount. This was not because they wanted to be a gaming company, but strictly to harvest first-party conversion data. They needed proprietary behavioral and return on ad spend data to train their Axon deep learning models because third-party advertisers wouldn't share it. Once the Axon 2.0 model launched in April 2023, it became so hyper-effective that the entire gaming industry had no choice but to plug in and share their data to access the platform's unparalleled returns. Having served its purpose, APP then shed the distraction, selling off the gaming portfolio to Tripledot Studios. This was pure strategic rent extraction. They used the asset to train the AI, built the moat, and immediately dumped the asset. Then there is the capital allocation execution. Back in 2022, when the broader market abandoned the stock and pushed APP's market cap to a floor of $3.8B, the business was still printing over $1 B in EBITDA. Instead of executing standard open-market share repurchases, management identified that their private equity backers and early insiders held nearly 50% of the float and eventually wanted liquidity. Management bypassed the public market entirely. They negotiated directly with those institutional holders to execute a massive $6 billion leveraged buyback. They effectively retired a huge portion of the company at rock-bottom valuations. Foroughi noted this singular move generated $50 to $60 B in retained value for remaining shareholders as the stock rebounded. While the rest of big tech hoards headcount, APP actively fired 40% of its workforce in recent years while growing revenue by nearly 100% YoY. The C-suite consists only of the CEO, CFO, CTO, and General Counsel. There is no CRO, no COO, and virtually no salesforce. They replaced standard enterprise bloat with LLMs. Over 80% of their codebase is now written by AI, multiplying the output of their highest-tier engineers up to 100x. The product nearly sells itself. Advertisers plug in, set a performance goal, and if the ROAS is positive, they scale spend infinitely. It turns advertisers into blind arbitrageurs. The TAM expansion is also clear. Now that the model has perfected gaming monetization, APP is unleashing Axon onto e-commerce and local SMBs. They don't need a massive sales team to do this. The AI matches the right intent with the right ad, pushing conversion rates up. The company literally has internal compensation triggers tied to a $1 T market cap. Given they have grown from under $4 B in 2022 to roughly ~$154 B in market cap today, betting against this lean, hyper-competent team does not make much sense.
7:31

There is a massive misunderstanding about what $APP actually is, and the recent Adam Foroughi interview just handed investors as a class in capital allocation, efficiency, and strategic vertical integration. At its core, AppLovin isn’t solely an ad-network or a gaming business but an arbitrage engine. APP is essentially run by a skeleton crew of about ~400 core engineers and product builders generating $1.3B in cash flow a quarter. The gaming studio acquisitions were purely strategic. From roughly 2018 to 2023, APP bought up mobile gaming studios. It was accumulating over 15 studios and 1,500 headcount. This was not because they wanted to be a gaming company, but strictly to harvest first-party conversion data. They needed proprietary behavioral and return on ad spend data to train their Axon deep learning models because third-party advertisers wouldn't share it. Once the Axon 2.0 model launched in April 2023, it became so hyper-effective that the entire gaming industry had no choice but to plug in and share their data to access the platform's unparalleled returns. Having served its purpose, APP then shed the distraction, selling off the gaming portfolio to Tripledot Studios. This was pure strategic rent extraction. They used the asset to train the AI, built the moat, and immediately dumped the asset. Then there is the capital allocation execution. Back in 2022, when the broader market abandoned the stock and pushed APP's market cap to a floor of $3.8B, the business was still printing over $1 B in EBITDA. Instead of executing standard open-market share repurchases, management identified that their private equity backers and early insiders held nearly 50% of the float and eventually wanted liquidity. Management bypassed the public market entirely. They negotiated directly with those institutional holders to execute a massive $6 billion leveraged buyback. They effectively retired a huge portion of the company at rock-bottom valuations. Foroughi noted this singular move generated $50 to $60 B in retained value for remaining shareholders as the stock rebounded. While the rest of big tech hoards headcount, APP actively fired 40% of its workforce in recent years while growing revenue by nearly 100% YoY. The C-suite consists only of the CEO, CFO, CTO, and General Counsel. There is no CRO, no COO, and virtually no salesforce. They replaced standard enterprise bloat with LLMs. Over 80% of their codebase is now written by AI, multiplying the output of their highest-tier engineers up to 100x. The product nearly sells itself. Advertisers plug in, set a performance goal, and if the ROAS is positive, they scale spend infinitely. It turns advertisers into blind arbitrageurs. The TAM expansion is also clear. Now that the model has perfected gaming monetization, APP is unleashing Axon onto e-commerce and local SMBs. They don't need a massive sales team to do this. The AI matches the right intent with the right ad, pushing conversion rates up. The company literally has internal compensation triggers tied to a $1 T market cap. Given they have grown from under $4 B in 2022 to roughly ~$154 B in market cap today, betting against this lean, hyper-competent team does not make much sense.

CapexAndChill

24,419 次观看 • 5 个月前

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1:35:26

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The All-In Podcast

194,430 次观看 • 4 个月前

Confluent just sold for $11 billion. Jay Kreps built it by learning a distinction that sits at the center of every hard company decision. There are two questions you can ask about anything hard: what can we do? And what do we have to do? The first is answered by your team. The second is imposed by the world. When Confluent needed a cloud product, most of the company thought it was a terrible idea. The on-prem business was working. The economics were better. Investors thought they were making a mistake. As Jay put it: if there were two standalone companies, we'd invest in this one and definitely not that one. Jay's answer: we have to do this. There's no question that a huge portion of the market is going to be in the cloud. So we have to serve that part of the market. The fact that it's very hard is not relevant. Once you know you have to do something, you find a way to do it. We cover this insight amongst dozens of others in my most recent "In Depth" conversation. Timestamps: 01:18 Making the leap from engineer to CEO 03:33 The 80% rule: what a CEO actually needs to know 04:54 Scaling different business disciplines 09:31 How Confluent’s story began in LinkedIn 12:13 The growing need for scalable data tech 13:37 What the early Kafka product looked like 16:38 Kafka’s underwhelming open-source launch 18:38 The blog post that accelerated Kafka’s adoption 20:16 Why so many marketing messages fail 28:08 The decision to build Confluent 34:24 Planning to fundraise before building the product 39:19 Confluent’s early years: Tough product decisions 47:07 The underrated growth lever question for companies 55:46 Why founder optimism is an overrated trait 1:00:29 What should founders give up as they scale? 1:02:47 Why people become trapped in a failure mindset 1:08:33 The Chipotle problem: Losing excellence at scale
1:14:36

Confluent just sold for $11 billion. Jay Kreps built it by learning a distinction that sits at the center of every hard company decision. There are two questions you can ask about anything hard: what can we do? And what do we have to do? The first is answered by your team. The second is imposed by the world. When Confluent needed a cloud product, most of the company thought it was a terrible idea. The on-prem business was working. The economics were better. Investors thought they were making a mistake. As Jay put it: if there were two standalone companies, we'd invest in this one and definitely not that one. Jay's answer: we have to do this. There's no question that a huge portion of the market is going to be in the cloud. So we have to serve that part of the market. The fact that it's very hard is not relevant. Once you know you have to do something, you find a way to do it. We cover this insight amongst dozens of others in my most recent "In Depth" conversation. Timestamps: 01:18 Making the leap from engineer to CEO 03:33 The 80% rule: what a CEO actually needs to know 04:54 Scaling different business disciplines 09:31 How Confluent’s story began in LinkedIn 12:13 The growing need for scalable data tech 13:37 What the early Kafka product looked like 16:38 Kafka’s underwhelming open-source launch 18:38 The blog post that accelerated Kafka’s adoption 20:16 Why so many marketing messages fail 28:08 The decision to build Confluent 34:24 Planning to fundraise before building the product 39:19 Confluent’s early years: Tough product decisions 47:07 The underrated growth lever question for companies 55:46 Why founder optimism is an overrated trait 1:00:29 What should founders give up as they scale? 1:02:47 Why people become trapped in a failure mindset 1:08:33 The Chipotle problem: Losing excellence at scale

Brett Berson

19,608 次观看 • 6 个月前

🚨 BIG INTERVIEW: SPENCER PRATT JOINS ALL-IN David Friedberg sits down with Spencer Pratt to discuss his fight to save Los Angeles. (0:00) Spencer Pratt vs. the Machine (3:01) Inside the Palisades Fire: Drained Reservoirs, No Sirens & Watching His House Burn on His Phone (14:03) Why He's Running for Mayor: FireAid's $100M Scandal & the NGO Corruption Nobody Talks About (28:10) Karen Bass at 20% & the Real State of LA: Crime, Homelessness & a City in Free Fall (38:23) Spencer's Plan to Fix LA: Enforcing Laws, Auditing Everyone & the Billionaires Ready to Rebuild (52:22) Hollywood, LAUSD & Small Business: What It Actually Takes to Make LA #1 Again (56:25) The Permitting Nightmare Killing Small Business & How AI Fixes It Overnight (1:04:22) His 8-Year Vision david friedberg Spencer Pratt --------------------------------------- Thanks to our partner for making this possible Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin — 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Advertiser access is in closed beta. The window is open at Axon by AppLovin
1:11:18

🚨 BIG INTERVIEW: SPENCER PRATT JOINS ALL-IN David Friedberg sits down with Spencer Pratt to discuss his fight to save Los Angeles. (0:00) Spencer Pratt vs. the Machine (3:01) Inside the Palisades Fire: Drained Reservoirs, No Sirens & Watching His House Burn on His Phone (14:03) Why He's Running for Mayor: FireAid's $100M Scandal & the NGO Corruption Nobody Talks About (28:10) Karen Bass at 20% & the Real State of LA: Crime, Homelessness & a City in Free Fall (38:23) Spencer's Plan to Fix LA: Enforcing Laws, Auditing Everyone & the Billionaires Ready to Rebuild (52:22) Hollywood, LAUSD & Small Business: What It Actually Takes to Make LA #1 Again (56:25) The Permitting Nightmare Killing Small Business & How AI Fixes It Overnight (1:04:22) His 8-Year Vision david friedberg Spencer Pratt --------------------------------------- Thanks to our partner for making this possible Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin — 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Advertiser access is in closed beta. The window is open at Axon by AppLovin

The All-In Podcast

764,407 次观看 • 4 个月前

My first interview with Scott Nolan, Co-Founder and CEO of General Matter. 0:48 What if Russia stopped selling uranium to the US 4:47 The history of uranium enrichment 10:44 What if he was given $25 billion to turn on reactors as fast as possible 13:37 The US doesn’t have the capability to make fuel for Small Modular Reactors today 14:43 How Founders Fund decides to start companies 19:28 Why you shouldn’t start a company 26:02 Working at SpaceX in 2003 28:10 How Elon drove urgency in the early days of SpaceX 30:28 How it felt when the first three Falcon launches failed 32:41 Figuring out what to work on after leaving SpaceX 34:26 How Peter Thiel convinced him to join Founders Fund 38:53 The most unintuitive thing he learned from Peter Thiel 44:03 Lowering the cost of uranium enrichment 47:32 The idiot index for enrichment 50:18 Vertically integrating vs using existing supply chains 53:44 The idiot index for timelines 59:13 Taking risk to pull in schedule: current burn vs future revenue 1:02:44 Choosing where to put their first enrichment facility 1:05:42 Maintaining maniacal urgency with multi-decade timelines 1:09:10 How a war with China will impact our ability to produce energy 1:14:06 What conventional wisdom gets wrong about building this type of company 1:17:48 The AI data center boom requires nuclear 1:21:02 Why SpaceX switched from an ablative engine to a regeneratively cooled all metal engine 1:25:08 How Elon makes nonobvious decisions 1:29:59 Don’t focus on problems that are already getting solved
1:34:02

My first interview with Scott Nolan, Co-Founder and CEO of General Matter. 0:48 What if Russia stopped selling uranium to the US 4:47 The history of uranium enrichment 10:44 What if he was given $25 billion to turn on reactors as fast as possible 13:37 The US doesn’t have the capability to make fuel for Small Modular Reactors today 14:43 How Founders Fund decides to start companies 19:28 Why you shouldn’t start a company 26:02 Working at SpaceX in 2003 28:10 How Elon drove urgency in the early days of SpaceX 30:28 How it felt when the first three Falcon launches failed 32:41 Figuring out what to work on after leaving SpaceX 34:26 How Peter Thiel convinced him to join Founders Fund 38:53 The most unintuitive thing he learned from Peter Thiel 44:03 Lowering the cost of uranium enrichment 47:32 The idiot index for enrichment 50:18 Vertically integrating vs using existing supply chains 53:44 The idiot index for timelines 59:13 Taking risk to pull in schedule: current burn vs future revenue 1:02:44 Choosing where to put their first enrichment facility 1:05:42 Maintaining maniacal urgency with multi-decade timelines 1:09:10 How a war with China will impact our ability to produce energy 1:14:06 What conventional wisdom gets wrong about building this type of company 1:17:48 The AI data center boom requires nuclear 1:21:02 Why SpaceX switched from an ablative engine to a regeneratively cooled all metal engine 1:25:08 How Elon makes nonobvious decisions 1:29:59 Don’t focus on problems that are already getting solved

Ti Morse

28,058 次观看 • 6 个月前

I asked a top 0.1% YouTube Strategist (Vexian - Algorithm Alchemist) to take me from 0 to 10m subscribers. This is every level of YouTube growth. 0:00 Education vs Entertainment 1:51 The Biggest Mistake New Creators Make 2:45 You Don't Need Gear You Need Reps 3:53 The One Thing That Guarantees 1K Subs 4:58 What Is the Impression Snowball 5:54 Consistency as Compound Interest 6:57 How to Come Up With Content Ideas 7:28 Track the Top 20% in Your Niche 8:18 Reverse Engineering Titles That Work 9:06 How to Remix Without Copying 10:00 Why People Actually Watch 11:30 90 Ideas in 30 Minutes 17:29 Level 2 (1K to 10K Subscribers) 21:00 How to Hook in 3 Seconds 24:40 YouTube Is a Zero Sum Game 25:11 Collabs and the Anchor Effect 26:48 Using Bigger Creators to Grow 28:21 What Separates Creators After 100K Videos 29:41 Consistency in Amount Type and Focus 30:05 How to Know When to Quit Your Niche 31:37 What Gaming YouTube Teaches Everyone 33:31 Storytelling From Minecraft YouTubers 34:22 Mini Stories and Escalating Stakes 37:47 Level 3 (10K to 100K Subscribers) 42:00 Package Your Video Like a Movie Poster 44:00 The Pre-Post Checklist 49:00 Seven Levels of Rapper and Viral Permutations 51:05 CTAs Where to Put Them and Where Not To 53:26 Subscribers Are a Vanity Metric 55:46 Replace Ad Reads With Your Own Content 58:49 Level 4 (100K to 1M Subscribers) 59:08 What Is Impact 1:00:01 Pattern Breaking and Absurdism 1:01:15 How to Title a Podcast for Absurdism 1:02:27 Melding Interest Topics Together 1:04:36 Parasocial Reciprocity 1:05:57 The Third Thing Nobody Talks About 1:06:05 What Is Sauce 1:08:20 How Long Does 100K Actually Take 1:09:31 When to Start Outsourcing 1:12:00 Your Niche Has a Ceiling 1:21:42 The Invisible Katana and 25M Views 1:23:27 One Word Across 5 Niches and 13 Years 1:25:38 What Channels Will Hit 1M 1:29:00 Level 5 (1M to 10M Subscribers) 1:29:34 Outlier vs Presence of Mind Ideation 1:30:50 Oppenheimer Barbie and the Minecraft Movie 1:38:00 Why the Best Channels Stop Growing 1:44:41 What Happens When You Beat YouTube 1:46:18 When You Become an Idea Not a Person 1:49:48 The Two Things That Actually Matter
1:52:04

I asked a top 0.1% YouTube Strategist (Vexian - Algorithm Alchemist) to take me from 0 to 10m subscribers. This is every level of YouTube growth. 0:00 Education vs Entertainment 1:51 The Biggest Mistake New Creators Make 2:45 You Don't Need Gear You Need Reps 3:53 The One Thing That Guarantees 1K Subs 4:58 What Is the Impression Snowball 5:54 Consistency as Compound Interest 6:57 How to Come Up With Content Ideas 7:28 Track the Top 20% in Your Niche 8:18 Reverse Engineering Titles That Work 9:06 How to Remix Without Copying 10:00 Why People Actually Watch 11:30 90 Ideas in 30 Minutes 17:29 Level 2 (1K to 10K Subscribers) 21:00 How to Hook in 3 Seconds 24:40 YouTube Is a Zero Sum Game 25:11 Collabs and the Anchor Effect 26:48 Using Bigger Creators to Grow 28:21 What Separates Creators After 100K Videos 29:41 Consistency in Amount Type and Focus 30:05 How to Know When to Quit Your Niche 31:37 What Gaming YouTube Teaches Everyone 33:31 Storytelling From Minecraft YouTubers 34:22 Mini Stories and Escalating Stakes 37:47 Level 3 (10K to 100K Subscribers) 42:00 Package Your Video Like a Movie Poster 44:00 The Pre-Post Checklist 49:00 Seven Levels of Rapper and Viral Permutations 51:05 CTAs Where to Put Them and Where Not To 53:26 Subscribers Are a Vanity Metric 55:46 Replace Ad Reads With Your Own Content 58:49 Level 4 (100K to 1M Subscribers) 59:08 What Is Impact 1:00:01 Pattern Breaking and Absurdism 1:01:15 How to Title a Podcast for Absurdism 1:02:27 Melding Interest Topics Together 1:04:36 Parasocial Reciprocity 1:05:57 The Third Thing Nobody Talks About 1:06:05 What Is Sauce 1:08:20 How Long Does 100K Actually Take 1:09:31 When to Start Outsourcing 1:12:00 Your Niche Has a Ceiling 1:21:42 The Invisible Katana and 25M Views 1:23:27 One Word Across 5 Niches and 13 Years 1:25:38 What Channels Will Hit 1M 1:29:00 Level 5 (1M to 10M Subscribers) 1:29:34 Outlier vs Presence of Mind Ideation 1:30:50 Oppenheimer Barbie and the Minecraft Movie 1:38:00 Why the Best Channels Stop Growing 1:44:41 What Happens When You Beat YouTube 1:46:18 When You Become an Idea Not a Person 1:49:48 The Two Things That Actually Matter

Grant

19,207 次观看 • 4 个月前

"We were go-karting and doing quite well. Now we've moved to Formula 1, and we're in the middle of the pack. We have a shot at the podium but we have to rewire for the race we're in." Akshay Kothari (Akshay Kothari). Cofounder and COO of Notion. Three years from now, most pre-AI companies will be gone. Notion will be one of the few standing stronger than before. This episode is a field study in how they're pulling it off. Knuckle Up ↓ 00:00 Intro 01:27 What were Notion's core founding principles? 06:20 Which early cultural principles scaled, and which broke? 08:22 How did Notion hire its first employees, and where did they come from? 11:48 How does hiring work now that the founders can't meet everyone? 14:35 Why does Akshay, as COO, prefer to have zero direct reports? 19:05 How do Ivan, Simon, and Akshay divide the work? 21:07 Does Notion's intentionality ever conflict with speed? 25:25 What should other founders steal from Notion's culture? 28:11 When did AI become a reason to rethink the whole product? 30:44 Why were the early AI years a "swamp of despair"? 36:05 How do you push AI across a huge product without losing the user? 39:25 Does Notion buy its AI DNA or build it? 40:44 Should Notion be afraid of OpenAI, Anthropic, and fast copycats? 46:58 What's hardest about the reinvention, and what does "meet the LLM" mean? 52:42 Is Notion AI-native in every function yet? 54:36 Are Notion's engineers still writing code, and how has engineering changed? 1:01:07 Once building is cheap, what's the new bottleneck? 1:02:39 How is AI reshaping sales, marketing, and support? 1:09:07 How many agents run inside Notion, and who builds them? 1:11:28 How has recruiting changed for the AI era? 1:13:48 What still worries Akshay about Notion's future? 1:15:22 Quickfire: admired founders, books, overrated AI advice, and Akshay’s superpower 1:19:42 What should a $50M pre-AI company do in the next 90 days?
1:22:10

"We were go-karting and doing quite well. Now we've moved to Formula 1, and we're in the middle of the pack. We have a shot at the podium but we have to rewire for the race we're in." Akshay Kothari (Akshay Kothari). Cofounder and COO of Notion. Three years from now, most pre-AI companies will be gone. Notion will be one of the few standing stronger than before. This episode is a field study in how they're pulling it off. Knuckle Up ↓ 00:00 Intro 01:27 What were Notion's core founding principles? 06:20 Which early cultural principles scaled, and which broke? 08:22 How did Notion hire its first employees, and where did they come from? 11:48 How does hiring work now that the founders can't meet everyone? 14:35 Why does Akshay, as COO, prefer to have zero direct reports? 19:05 How do Ivan, Simon, and Akshay divide the work? 21:07 Does Notion's intentionality ever conflict with speed? 25:25 What should other founders steal from Notion's culture? 28:11 When did AI become a reason to rethink the whole product? 30:44 Why were the early AI years a "swamp of despair"? 36:05 How do you push AI across a huge product without losing the user? 39:25 Does Notion buy its AI DNA or build it? 40:44 Should Notion be afraid of OpenAI, Anthropic, and fast copycats? 46:58 What's hardest about the reinvention, and what does "meet the LLM" mean? 52:42 Is Notion AI-native in every function yet? 54:36 Are Notion's engineers still writing code, and how has engineering changed? 1:01:07 Once building is cheap, what's the new bottleneck? 1:02:39 How is AI reshaping sales, marketing, and support? 1:09:07 How many agents run inside Notion, and who builds them? 1:11:28 How has recruiting changed for the AI era? 1:13:48 What still worries Akshay about Notion's future? 1:15:22 Quickfire: admired founders, books, overrated AI advice, and Akshay’s superpower 1:19:42 What should a $50M pre-AI company do in the next 90 days?

Nakul Mandan

205,031 次观看 • 4 个月前

My first interview with Eric Jorgenson 📚 ☀️, Author of The Book of Elon. 0:12 Why Elon outcompetes everyone 1:53 Not wanting to be CEO 6:15 Why there was a 1 in 100 chance of Tesla and SpaceX both succeeding 10:03 Thinking in lost future revenue 11:58 Living with a gun next to his bed 15:02 How Elon starts companies 19:23 Giving Larry Page an early demo of the Roadster 21:27 Why Elon calls people into the factory at 2am 25:01 The 20 people who execute Elon’s will autonomously - MrBeast clones 28:52 Working with Elon is doing a tour of duty 31:18 Creating a grand vision that people can believe in 36:42 How to thrive under Elon 37:30 The Algorithm - question every requirement, delete part or process, simplify and optimize, accelerate, automate 41:38 Setting up a tent factory in the Tesla Fremont parking lot 43:28 How Elon would play the game if he was born 300 years ago 45:10 Empathy for the individual vs empathy for the mission 46:57 Being feared vs being loved 48:38 The endless churn at Elon companies 50:49 Ruthlessness and having a willingness to be disliked 56:08 Creating enemies and chaos 58:37 Shifting SpaceX’s focus from Mars to building a base on the Moon 1:00:07 Justifying Tesla’s valuation by reseting the vision 1:02:21 The S-Curve of ambition - colonizing planets, Starlink, Terafab 1:04:44 Throwing billions of dollars behind new companies 1:06:22 A great team is just the sum of the individual vectors 1:11:09 The idiot index at an industry-scale 1:13:53 Why it’s critical for space companies to control launch 1:15:44 Thinking in limits 1:18:47 Expanding the vision until no one can measure it 1:23:41 Internalizing pain and dealing with setbacks 1:26:03 The evolution of Elon’s operating philosophy over the last 30 years 1:27:37 Effectiveness
1:29:15

My first interview with Eric Jorgenson 📚 ☀️, Author of The Book of Elon. 0:12 Why Elon outcompetes everyone 1:53 Not wanting to be CEO 6:15 Why there was a 1 in 100 chance of Tesla and SpaceX both succeeding 10:03 Thinking in lost future revenue 11:58 Living with a gun next to his bed 15:02 How Elon starts companies 19:23 Giving Larry Page an early demo of the Roadster 21:27 Why Elon calls people into the factory at 2am 25:01 The 20 people who execute Elon’s will autonomously - MrBeast clones 28:52 Working with Elon is doing a tour of duty 31:18 Creating a grand vision that people can believe in 36:42 How to thrive under Elon 37:30 The Algorithm - question every requirement, delete part or process, simplify and optimize, accelerate, automate 41:38 Setting up a tent factory in the Tesla Fremont parking lot 43:28 How Elon would play the game if he was born 300 years ago 45:10 Empathy for the individual vs empathy for the mission 46:57 Being feared vs being loved 48:38 The endless churn at Elon companies 50:49 Ruthlessness and having a willingness to be disliked 56:08 Creating enemies and chaos 58:37 Shifting SpaceX’s focus from Mars to building a base on the Moon 1:00:07 Justifying Tesla’s valuation by reseting the vision 1:02:21 The S-Curve of ambition - colonizing planets, Starlink, Terafab 1:04:44 Throwing billions of dollars behind new companies 1:06:22 A great team is just the sum of the individual vectors 1:11:09 The idiot index at an industry-scale 1:13:53 Why it’s critical for space companies to control launch 1:15:44 Thinking in limits 1:18:47 Expanding the vision until no one can measure it 1:23:41 Internalizing pain and dealing with setbacks 1:26:03 The evolution of Elon’s operating philosophy over the last 30 years 1:27:37 Effectiveness

Ti Morse

126,252 次观看 • 5 个月前

🎧🍌 New The Peel with Ankur Goyal Ankur is the Founder and CEO of @braintrustdata, the end to end developer platform for building the world's best AI products. Their customers include companies like Instacart, Zapier, Notion, Airtable, Replit, and more. We hit on the importance of LLM evals, advice for building AI products, why the best companies have two AI product roadmaps, how he hates meetings, and all his non-conventional advice for founders. Watch below or links in the replies! Timestamps: 04:04 Why everyone’s now an AI company 06:03 Reasons LLM evals are so important 09:19 Replacing vibe checks with Braintrust 10:37 Making OpenAI’s protocols the standard 11:27 Why the best companies have two AI roadmaps 13:06 Build your product so each LLM release makes it better 14:54 Predicting AGI is impossible 15:54 Why people who work with LLMs aren’t worried about AI safety 16:52 The best developers are all-in on co-pilots 18:11 How AI is changing software development 21:09 Combining IDE, CI/DC, and observability in one product 27:18 Are models more like CPU’s or relational databases? 30:14 How to pick an LLM 33:00 Advice for staying on top of new AI developments 34:30 Why tool calling is so important 38:02 Advice for young software engineers 40:25 Learning to code doing linear algebra homework 42:36 Lack of purpose interning in big tech 44:07 Working at MemSQL learning to be a founder 47:52 How to get a job at a startup 50:43 Building his first startups product on an flight 52:39 Three lessons from his first failed startup 54:46 Don’t delegate what you’re good at 55:46 Why you should be careful listening to VCs advice 57:34 Tactics for successful delegation 59:36 Why Ankur hates meetings 1:02:42 The importance of self-service in unlocking certain customer segments 1:05:14 How Braintrust got started 1:07:45 Advice on picking your target customers 1:10:35 How Braintrust hires with work trials 1:15:21 Balancing security with a modern UI 1:17:49 Why it’s hard to sell non-AI products right now 1:19:21 Advice for selling to large enterprises 1:23:10 Ankur’s favorite AI products
1:27:03

🎧🍌 New The Peel with Ankur Goyal Ankur is the Founder and CEO of @braintrustdata, the end to end developer platform for building the world's best AI products. Their customers include companies like Instacart, Zapier, Notion, Airtable, Replit, and more. We hit on the importance of LLM evals, advice for building AI products, why the best companies have two AI product roadmaps, how he hates meetings, and all his non-conventional advice for founders. Watch below or links in the replies! Timestamps: 04:04 Why everyone’s now an AI company 06:03 Reasons LLM evals are so important 09:19 Replacing vibe checks with Braintrust 10:37 Making OpenAI’s protocols the standard 11:27 Why the best companies have two AI roadmaps 13:06 Build your product so each LLM release makes it better 14:54 Predicting AGI is impossible 15:54 Why people who work with LLMs aren’t worried about AI safety 16:52 The best developers are all-in on co-pilots 18:11 How AI is changing software development 21:09 Combining IDE, CI/DC, and observability in one product 27:18 Are models more like CPU’s or relational databases? 30:14 How to pick an LLM 33:00 Advice for staying on top of new AI developments 34:30 Why tool calling is so important 38:02 Advice for young software engineers 40:25 Learning to code doing linear algebra homework 42:36 Lack of purpose interning in big tech 44:07 Working at MemSQL learning to be a founder 47:52 How to get a job at a startup 50:43 Building his first startups product on an flight 52:39 Three lessons from his first failed startup 54:46 Don’t delegate what you’re good at 55:46 Why you should be careful listening to VCs advice 57:34 Tactics for successful delegation 59:36 Why Ankur hates meetings 1:02:42 The importance of self-service in unlocking certain customer segments 1:05:14 How Braintrust got started 1:07:45 Advice on picking your target customers 1:10:35 How Braintrust hires with work trials 1:15:21 Balancing security with a modern UI 1:17:49 Why it’s hard to sell non-AI products right now 1:19:21 Advice for selling to large enterprises 1:23:10 Ankur’s favorite AI products

Turner Novak 🍌🧢

44,280 次观看 • 2 年前

"A week is 2% of the year. The cadence we operate on is: A week is not the shortest timeline. It's the longest timeline. What can we decide by tomorrow?" - Michael Grinich. Michael Grinich. Founder and CEO of WorkOS. The company powering the enterprise adoption of AI. Knuckle Up ↓ -- 00:00 Introduction 01:33 From design-obsessed founder to enterprise infrastructure 04:20 Michael’s year off and what made the WorkOS bet obvious 06:54 Why a great startup idea has to look bad first 09:46 Minimum awesome product beats MVP 11:09 The org with no CRO, no VP of sales, and one PM 13:29 Hiring for curiosity, not credentials 16:25 The "AI pilled" interview red flag 18:25 A week is 2% of the year 26:00 How WorkOS approaches brand 33:00 The future shape of engineering orgs 43:20 Why senior engineers benefit most from AI 44:45 Micro-leadership over micromanagement 49:10 Tough times in the early days 59:04 The reverse Peter principle 1:04:38 Quickfire: red flags, hires too early, and biggest fears 1:10:30 Michael's advice to his 25-year-old self
1:12:20

"A week is 2% of the year. The cadence we operate on is: A week is not the shortest timeline. It's the longest timeline. What can we decide by tomorrow?" - Michael Grinich. Michael Grinich. Founder and CEO of WorkOS. The company powering the enterprise adoption of AI. Knuckle Up ↓ -- 00:00 Introduction 01:33 From design-obsessed founder to enterprise infrastructure 04:20 Michael’s year off and what made the WorkOS bet obvious 06:54 Why a great startup idea has to look bad first 09:46 Minimum awesome product beats MVP 11:09 The org with no CRO, no VP of sales, and one PM 13:29 Hiring for curiosity, not credentials 16:25 The "AI pilled" interview red flag 18:25 A week is 2% of the year 26:00 How WorkOS approaches brand 33:00 The future shape of engineering orgs 43:20 Why senior engineers benefit most from AI 44:45 Micro-leadership over micromanagement 49:10 Tough times in the early days 59:04 The reverse Peter principle 1:04:38 Quickfire: red flags, hires too early, and biggest fears 1:10:30 Michael's advice to his 25-year-old self

Nakul Mandan

671,989 次观看 • 4 个月前